doi:10.1093/brain/awl250 Brain (2006), 129, 2571–2584
Making non-fluent aphasics speak: sing along!
Ame´lie Racette,1
Ce´line Bard2
and Isabelle Peretz1
1
Department of Psychology, University of Montreal and 2
Department of Radiology, Centre Hospitalier Universitaire de
Montre´al, Montreal, Quebec, Canada
Correspondence to: Isabelle Peretz, PhD, De´partement de psychologie, Universite´ de Montre´al, Pavillon Marie-Victorin,
local D-418, 90, avenue Vincent D’Indy, Montreal, Quebec, H2V 2S9, Canada
E-mail: isabelle.peretz@umontreal.ca
A classic observation in neurology is that aphasics can sing words they cannot pronounce otherwise. To further
assess this claim, we investigated the production of sung and spoken utterances in eight brain-damaged patients
suffering from a variety of speech disorders as a consequence of a left-hemisphere lesion. In Experiment 1, the
patients were tested in the repetition and recall of words and notes of familiar material. Lyrics of familiar songs,
as well as words of proverbs and prayers, were not better pronounced in singing than in speaking. Notes were
better produced than words. In Experiment 2, the aphasic patients repeated and recalled lyrics from novel
songs. Again, they did not produce more words in singing than in speaking. In Experiment 3, when allowed to
sing or speak along with an auditory model while learning novel songs, aphasics repeated and recalled more
words when singing than when speaking. Reduced speed or shadowing cannot account for this advantage of
singing along over speaking in unison. The results suggest that singing in synchrony with an auditory model—
choral singing—is more effective than choral speech, at least in French, in improving word intelligibility because
choral singing may entrain more than one auditory–vocal interface. Thus, choral singing appears to be an
effective means of speech therapy.
Keywords: aphasia; singing; speech; melodic intonation therapy; music
Abbreviations: MIT ¼ Melodic Intonation Therapy
Received June 26, 2006. Revised August 11, 2006. Accepted August 14, 2006. Advance Access publication September 7, 2006.
Introduction
Singing is considered to be an effective means for non-fluent
aphasics to produce words that they are not able to
pronounce otherwise (Keith and Aronson, 1975; Assal et al.,
1977; Yamadori et al., 1977; Jacome, 1984). Singing may
facilitate speech at different stages of processing: at the motor
stage by reducing the speech rate in dysarthric patients
(Yorkston and Beukelman, 1981; Yorkston et al., 1990;
Hustad et al., 2003), at the level of word retrieval by providing
structural constraints, such as the number of syllables per beat
(Wallace, 1994; Rubin, 1995; Poulin-Charronat et al., 2005)
or at a motivational level by engaging recreational skills.
Identification of the factors that make singing an effective
treatment for speech disorders is important to determine who
can be helped by musical interventions and why.
The use of music as a therapy for speech has given birth to
the Melodic Intonation Therapy (MIT). MIT is a ‘rehabilitation
program using high probability phrases and
sentences which are intoned and tapped out in a syllableby-syllable
manner’, mainly used with patients with severe
expressive deficits (Sparks et al., 1974; Naeser and HelmEstabrooks,
1985). An early interpretation of successful
recovery from aphasia with the MIT technique was that it
facilitated the use of homologous language areas of the right
hemisphere after damage to the language areas in the left
hemisphere (Albert et al., 1973). However, a study by Belin
et al. (1996) challenged this notion in showing that
repetition of words trained with MIT elicited a deactivation
in the right-hemisphere structures while language-related
left-hemisphere structures were active in seven non-fluent
aphasic patients who were successfully treated with MIT.
One possible explanation for this discrepancy is that the
American use of the MIT emphasizes melodic cues whereas
the French intervention stresses rhythm.
The rhythmic support seems to be key (Boucher et al.,
2001). MIT treatments emphasizing rhythm (with stressing
points emphasized by the spoken syllable /tae/, and handtapping)
lead to better syllable repetition than treatments
emphasizing intonation (tone contour and melodic intonation).
Another advantage related to rhythm is that singing
slows down the rate of word production and thereby may
improve intelligibility (Laughlin et al., 1979; Pilon et al.,
1998). Intelligibility is tightly related to speech rate as
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observed in three dysarthric patients with three different
pacing methods (Pilon et al., 1998). The slower the pace
imposed on speech production, the better the intelligibility,
but only when the speech reduction was severe. Because
singing slows down word production by 50% as compared
with speaking (Kilgour et al., 2000), singing should be
effective in improving intelligibility.
An additional reason why singing could improve speech is
that words learned through music, as in familiar songs, are
fixed or non-generative. Automatic or non-propositional
speech, which includes not only song lyrics but also prayers
and swearing, is usually preserved in non-fluent aphasia
(Ryding, et al., 1987; Speedie et al., 1993; Blank et al., 2002;
Van Lancker-Sidtis, 2004). Moreover, in songs, words and
melodies are learned together. Melody may facilitate access
to words because they are tightly associated in memory
(Serafine et al., 1984, 1986; Crowder et al., 1990; Peretz et al.,
2004b).
In sum, there are many reasons why singing can improve
language production. Nevertheless, supporting evidence is
scant. Cohen and Ford (1995) examined the production of
songs by 12 patients who became aphasic after a unilateral
left-hemisphere vascular accident, under three experimental
conditions: naturally spoken, spoken with a steady drumbeat
accompaniment and sung with the melody played on a
keyboard. They found that speech content and error types
did not differ across conditions, but that word intelligibility
was higher when utterances were spoken without any
support. However, it may be the case that the patients could
not benefit from the musical aid because of associated
musical disorders. Indeed, aphasia and amusia often occur
together (Marin and Perry, 1999). Yet, even when music
processing is preserved, we found that singing did not help
speech recovery in two single-case studies (He´bert et al.,
2003; Peretz et al., 2004a). The results suggest that verbal
production, be it sung or spoken, is mediated by the same
(impaired) language output system and that this speech
route is distinct from the (spared) melodic route. Thus, the
classic reports that non-fluent aphasics are able to sing may
simply reflect the dissociation between automatic speech (in
singing) and propositional speech (in spontaneous speech).
However, our two aphasic patients (He´bert et al., 2003;
Peretz et al., 2004a) presented atypical forms of aphasia (i.e.
crossed aphasia and primary progressive aphasia, respectively).
The goal of the present study was to examine this
issue in common speech disorders.
To this aim, we conducted a multiple-case study with
aphasic patients whose performance was compared when
singing and reciting both familiar and novel utterances.
A variety of speech deficits were considered to allow the
exploration of a wide range of error types in production.
In Experiment 1, we investigated patients’ production of
familiar material, such as traditional songs, prayers, proverbs
and rhymes. If automatic access to words in memory is
critical, then the aphasic patients should be fluent with all
verbal material, be it spoken or sung. The patients not only
had to recite the lyrics of the songs, but they also had to sing
the words of prayers and proverbs on a familiar melody. If
singing helps word production, then sung words should be
more accurate than spoken words in both lyrics and wellknown
expressions.
In order to assess the effect of singing on word
production when access to the words is not automatic, we
tested the patients with novel songs in Experiments 2 and 3.
Furthermore, patients learned the novel songs either alone
(Experiment 2) or in unison (Experiment 3). Unison production
was performed at two different speeds in order to
explore the role of speech rate on word intelligibility. Unison
production should improve performance when compared
with speaking or singing alone, simply because it provides
online cues for the words to vocalize. ‘Choral speech’ is
known to improve speech fluency in people who stutter
(Saltuklaroglu et al., 2004). The aid should be particularly
effective at slow speed.
General method
Participants
Eight non-fluent aphasics were recruited through the Quebec
Association of Persons with Aphasia (AQPA). A summary of
the patients’ characteristics is given in Table 1. All participants
are right-handed, French-speaking and suffered a
left cerebral vascular accident at least 2 years before the
study. No patient had pre-morbid history of neurological
or psychiatric problems. CT scans of four patients and MRI
scans of three patients were obtained at the time of testing
(summer 2002, except for JH’s scan that was taken after
his stroke in 1997). Informed consent was obtained from
all patients and the study was approved by the Ethical
Committee of the Montreal University Geriatric Institute.
The patients were assessed and tested in French, their
native language. All patients had undergone speech therapy.
At the time of testing, the patients were re-evaluated by
two independent speech therapists, using the MT-86
(Nespoulous et al., 1992), the short version of the Token
Test (De Renzi and Vignolo, 1962) and subtests of the
French version of the Boston Diagnostic Aphasia Examination
(Mazaux and Orgogozo, 1981). On the basis of these
tests, the patients were diagnosed as suffering from Broca’s
aphasia (JS, LB, RD), mixed aphasia with predominance of
expressive deficits (RH, PP, CA, JH) and anomia (LD; see
Table 1). In addition to the language problem, most cases
(except JH and LD) suffered from dysarthria, an articulatory
problem due to impaired coordination of speech muscles,
and buccofacial apraxia, an inability to coordinate and
carry out facial and lip movements. The main diagnostic
scores are presented in Table 2. As can be seen, the
speech disorders were mostly expressive, affecting both oral
and written language. Simple comprehension was well
preserved. Limited verbal expression, preserved comprehension
and intact right hemisphere made all the patients
eligible for MIT, even if some were better candidates (e.g.
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LB, JS, RD) than others (e.g. LD), because of their
severe speech reduction. Individual scores are ordered in
the Tables from the most to the less severe cases of speech
reduction.
In order to assess prosody, the patients and three
neurologically intact participants were required to produce
three neutral sentences with four different intonations:
statement, question, joy and sadness. The renditions were
Table 2 Language assessment
LB PP JS JH CA RD RH LD
MT-86 aphasia battery
Expression
Naming 0/6 11/31 26/31 17/31 30/31 31/31 28/31 28/31
Verbal fluency 3 0 13 15 35 38 32 36
No agrammatism
Automatized speech
Digits 1 to 10 n n n n n n n Slow
Days of the week n– d n n n n n Slow
Months of the year – d n n n n n Slow
Words of familiar songs d d d n n– n n n–
Melody of familiar songs n n n n n– n n n–
Repetition
Syllables 5/20 19/20 3/20 20/20 16/20 18/20 11/20 20/20
Words and non-words 10/30 22/30 17/30 30/30 28/30 27/30 21/30 30/30
Sentences 0/3 0/3 0/3 2/3 1/3 1/3 0/3 1/3
Auditory comprehension
Words 9/9 9/9 9/9 9/9 8/9 9/9 – 9/9
Simple sentences 6/6 6/6 5/6 6/6 6/6 6/6 6/6 6/6
Complex sentences 24/32 25/32 23/32 28/32 24/32 24/32 32/32 28/32
Body-part identification 8/8 7/8 8/8 8/8 8/8 8/8 8/8 9/9
Object manipulation 5/8 5/8 5/8 6/8 5/8 7/8 8/8 7.5/8
Reading comprehension 5/9 14/20 16/23 15/23 17/23 18/23 23/23 13/13
Dictation – 3/7 4/9 0 2/4 1/6 23/35 n
Token test 16/36 10.5/36 13.5/27 22/36 16.5/28 28.5/36 29/36 26.5/36
n = normal performance; n- = with help; d = deficit; – = not assessed.
Table 1 Characteristics of patient’s diagnosis, aetiology and lesion location
Patient Sex Age Education Years since
last infarct
Number
of infarcts
Diagnosis Aetiology Lesion location
LB F 56 10 5 1 Severe Broca’s aphasia
Severe dysarthria
Left sylvian CVA
PP M 45 11 6 2 Severe mixed aphasia Left sylvian CVA
JS M 55 18 20 1 Severe Broca’s aphasia Left frontoparietal
aneurysm
Thalamic lacuna cerebri
JH M 62 20 5 1 Moderate to severe mixed
aphasia;
Predominantly expressive
deficit
Left sylvian CVA
CA F 36 14 13 1 Moderate to severe mixed
aphasia;
Predominantly expressive
deficit
Aneurysm of the
anterior and mid-left
cerebral artery
RD F 54 9 19 1 Moderate Broca’s aphasia Left sylvian CVA
RH M 67 7 9 2 Atypical Broca’s aphasia;
severe dysarthria
Left sylvian CVA
LD F 38 15 19 1 Mild to moderate anomia Left sylvian CVA
The right side of the brain is on the left; CVA = cerebral vascular accident.
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randomly mixed and presented to five judges who had to
guess and rate the intended intonation. The aphasic patients
scored lower than controls for at least one intonation,
especially joy. The fact that aphasics had pronunciation
difficulties might have played a role.
Each participant was tested with a short neuropsychological
battery of tests, including the digit span, the standard
non-coloured Raven’s matrices (1996) and the Tower of
London (Shallice, 1982; see Table 3). Digit spans were
limited, probably because of the alteration of verbal rehearsal
(Burgio and Basso, 1997). Raven’s matrices revealed good
reasoning abilities in half of the participants. The other
half was impaired. This is not too surprising since lefthemisphere
lesions, especially in the frontal region, have
been associated with difficulties in reasoning (Langdon and
Warrington, 2000). Disorders of executive functions, as
revealed by planning difficulties in the Tower of London,
were present in JS, and to a lesser degree in PP.
Regarding musical abilities, it is worth mentioning
that five aphasics (LB, PP, JS, CA, RH) participate in the
choir activity organized by their association for 2 h a week.
The choir activity consists in singing along familiar tunes.
None of the participants had formal musical training. Their
musical perception abilities were assessed with the Montreal
Battery for Evaluation of Amusia (MBEA; Peretz et al.,
2003). The scores are presented in Table 4. Five participants
had normal scores, whereas two participants (PP and RD)
were considered amusic because their composite scores were
2 SD below the mean of normal controls. PP’s performance
reflects a disorder in the temporal organization of music.
RD’s amusia is more severe; however, her residual pitch and
temporal abilities, as revealed in the repetition of familiar
melodies (see Experiment 1), seem sufficient to support
singing.
General procedure and data analysis
The patients participated in about four sessions of 2 h each.
Sessions were adapted to the patients’ capacities with flexible
durations, pauses and at-home testing. The testing sessions
were recorded on a DAT Sony recorder via a Shure
565SD microphone. All productions were saved in digital
sound files.
The experimenter (AR) and a university student in
speech–language pathology transcribed all the produced
words for scoring purpose. Words were considered correct
or incorrect, irrespectively of their pitch and duration. Words
were chosen over syllables because number of syllables
sometimes differs across conditions; mute vowels are often
sung but not pronounced. Contractions, such as articles or
short words that were not produced on a distinct note, were
considered as part of the word to which they were attached.
A point was given for every word correctly produced in the
right order. Recognizable (because of mild phonemic errors)
words were given half a point. Omissions and substitutions
were given no point. The number of correct words
corresponded to the number of words for which both
judges gave a point. The words on which they disagreed were
discarded. The score corresponded to the number of words
for which both judges gave a point or half a point, divided
by the total number of words both judges agreed on and
multiplied by 100, for each song line. A total of 4957, 1835
and 4960 words were analysed in Experiments 1, 2 and 3,
with inter-rater agreements of 92, 96 and 88%, respectively.
The experimenter (AR) and a music faculty student
transcribed by ear and scored the musical productions. Pitch
intervals and directions were analysed, but not rhythm
because of the numerous pauses or hesitations. The number
of correct notes corresponded to the number of notes for
Table 4 Patients’ score on the MBEA
Patient Scale Contour Interval Rhythm Metre Memory Composite score
LB 29 25 26 26 26 25 26
PP 27 25 24 21* 18* 16* 22*
JS 26 24 24 26 27 20* 25
JH 24 29 21 30 22 25 25
CA 24 26 22 28 26 25 25
RD 19* 21* 18* 22* 20 15* 19*
RH 30 28 29 27 20 24 26
LD 27 24 25 30 23 26 26
Cut-off score 22 22 21 23 20 22 23
The maximal score on each test is 30 (chance is 15).
*Below cut-off.
Table 3 Neuropsychological assessment
Patient Digit span Raven matrices
(percentile)
Tower of London
mean number of movements
(mean total time in seconds)*
LB 3 48/60 (41 p.) 8.3 (26.3)
PP 2 52/60 (50 p.) 9.75 (30.75)
JS 3 27/60 (<5 p.) 10.7 (100.7)
JH 4 35/60 (10 p.) 5.2 (25.7)
CA 3 42/50 (11 p.) 6.4 (33.8)
RD 4 41/60 (12 p.) 7 (38.5)
RH 4 39/60 (25 p.) 5.4 (21.7)
LD 3 52/60 (50 p.) 5.1 (18)
*The standard norms for the Tower of London are M = 5.75,
SE = 1.49 for the mean number of movements and M = 24.67,
SE = 24.5 for the mean total time.
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which both judges gave a point. When there was a disagreement,
the note was discarded. The musical score
corresponded to the total of correct pitches divided by the
total number of possible notes minus the notes both raters
disagreed upon for these productions, multiplied by 100.
A total of 7717, 1410 and 3398 musical notes were analysed
in Experiments 1, 2 and 3, with inter-rater agreements of 95,
85 and 79%, respectively.
Because of the limited number of patients and the
variability between subjects, non-parametric tests were used,
with an alpha level of 0.05.
Experiment 1: Production of
familiar material
Method
Patients were presented with two types of material. The
first material consisted of 14 familiar songs selected from
children and traditional songs (Peretz et al., 1995). Song
excerpts were, on average, 5 words (range: 3–7) and 9 notes
(6–11) long. The second material was composed of two
well-known prayers (‘Our Father’ and ‘Holy Mary’), six
proverbs (e.g. ‘All roads lead to Rome’) and one nursery
rhyme (e.g. ‘Eenie, meenie, minie, mo’). We will refer to
these selected non-musical over-learned expressions as
‘verbatim speech’. The sentences were, on average, 7 words
(6–10) and 8.5 notes (7–12) long.
Patients had to perform two tasks on these materials:
a repetition task and a recall from memory task. In the
repetition task, the experimenter gave the first line and the
participant had to repeat it in the same mode (for examples,
see www.brams.umontreal.ca/peretz/). For sung repetition,
the verbatim speech lines were sung to a familiar melody.
Eight familiar songs that matched the number of syllables in
the line were used. The second task implied recall from
memory: the titles were presented to participants, who had
to produce as much as they knew of the song, prayer or
rhyme. For the proverbs, the beginning of the expression was
given (e.g. An apple a day. . .) and the patient completed it
(. . . keeps the doctor away). The recall of verbatim speech
was always performed before the repetition task. Otherwise,
order of presentation was counter-balanced across participants.
At the end, spontaneous production and repetition
of the songs’ melodies on the syllable /la/ were assessed in
one single block, in a counter-balanced order. Throughout
the experiment, live presentation was used, hence enabling
the use of visual as well as auditory cues. Patients could also
ask the experimenter to repeat the title or the line in order to
achieve his/her best performance (usually two to four times).
Results
Individual scores, corresponding to the percentage of correct
words or notes produced and obtained for the different
songs and for the different verbatim expressions, were
averaged for each participant and for each task (repetition
and recall; see Table 5) and compared with Wilcoxon tests.
The results will first be presented for songs and then
for verbatim speech. For these two types of automatic
productions, two conditions (isolated, combined) and two
tasks (repetition, recall) will be compared for words and
for musical notes. The isolated condition refers to the
production of words only or musical notes only, while the
combined condition refers to the sung production of both
words and notes. Error types in word production were
also assessed and compared between the sung and spoken
conditions.
Songs
As can be seen in Table 5, the percentage of words correctly
produced was not higher in singing than in speaking, in both
repetition, Z = 0.34, n.s., and recall, Z = 0.56, n.s. This held
true for each patient when considering song excerpts as the
random variable (all P > 0.05, by Wilcoxon tests). Only one
patient (LB) produced more words from memory in singing
than in speaking, Z = 1.96, P = 0.05. The correlation between
participants’ word scores in singing and speaking was
significant in repetition, r(8) = 0.99, P < 0.001, and in recall,
r(8) = 0.89, P < 0.01.
The most frequent errors committed by the patients
were non-words which share phonemes with the target
word (phonemic paraphasias: 39%). The other errors
Table 5 Percentages of correct word and note
production for familiar songs
Patient Repetition Recall
Combined Isolated Combined Isolated
LB
Words 54 56 50 34
Notes 90 98 90 85
PP
Words 34 38 24 16
Notes 83 81 43 41
JS
Words 13 10 6 10
Notes 98 91 80 24
JH
Words 93 93 73 71
Notes 100 98 94 95
CA
Words 91 83 66 69
Notes 93 85 70 72
RD
Words 89 90 65 72
Notes 88 82 88 67
RH
Words 85 89 66 72
Notes 96 92 83 64
LD
Words 84 78 49 77
Notes 74 63 31 18
Mean
Words 67.9 67.1 49.9 52.6
Notes 90.3 86.3 72.4 58.3
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were omissions (31%), neologisms (12%) and real words
semantically related to the target word (semantic paraphasia:
11%). There were very few real words with no semantic
relation with the target word (lexical paraphasias: 6%). In
repetition, the error types were similar in singing and
speaking (all P > 0.05), except for the phonemic errors, which
were more frequent in singing (45%) than in speaking (33%),
Z = 1.96, P < 0.05. This was true for all but one participant
(RH) who suffers from a severe dysarthric problem.
For all patients but LD, production of musical notes was
much easier than words. This was confirmed statistically in
singing songs in both repetition, Z = 1.82, P = 0.07, and
recall, Z = 2.10, P < 0.05 (see Words versus Notes Combined
in Table 5). A similar trend was apparent when participants
were producing musical notes or words alone, although
it did not reach significance. None of the correlations
computed between word and note scores reached significance
(all P > 0.05). LD’s discrepancy between repetition and
recall scores for notes seems to reflect a production
deficiency rather than a perceptual or memory difficulty.
In general, word performance was higher in repetition
than in recall, both in singing, Z = 2.52, P < 0.05, and in
reciting, Z = 2.38, P < 0.05, although the three patients with
the most severe form of aphasia (LB, PP, JS) had very low
scores in both tasks. Similarly, the percentage of musical
notes correctly produced was higher in repetition than
in recall, both when produced alone, Z = 2.52, P < 0.05,
and with words, Z = 2.20, P < 0.05. While sung words were
not better performed than recited words, sung notes were
more accurate when sung with words than on /la/ in recall,
Z = 2.03, P < 0.05. The same tendency was present in
repetition but failed to reach significance, Z = 1.61, n.s.
Verbatim speech
Comparing the sung and spoken production of popular
expressions is interesting because it is usually the goal
of speech therapy to improve production of functional
sentences such as ‘Thank you’. However, the present results
suggest that using songs is not very effective (see Table 6).
No effect of condition was obtained in repetition of
verbatim speech, Z = 0.98, n.s. The only patient (LB) to
show a positive effect of singing, with Z = 2.06, P < 0.05, is
the same patient who benefited from singing songs. Here,
LB was unable to recite a single word. However, two other
patients (JS, RD) exhibited the reverse pattern, with spoken
repetitions being better than sung repetitions, Z = 2.03 and
2.20, P < 0.05. JS was unable to repeat the expressions in
singing. For these patients, executive impairments might
explain the difficulty to sing conventionally spoken expressions
to familiar melodies. The other participants had similar
performance in the two modalities (all P > 0.05).
Here, omission was the most frequent error (53%).
Phonemic paraphasias (21%) were again more frequent than
semantic paraphasias (3%). Comparisons of the proportions
of each error type in the sung and the spoken conditions
revealed only one significant difference for neologisms
(e.g. tantan for claire). These errors were more frequent
in singing (14.3%) than in speaking (10.6%; Z = 1.99,
P < 0.05). For both materials, the errors respected the
number of syllables of the words in 92% of the phonemic
paraphasias, thus preserving the rhythmic structure of the
line, both in singing and in reciting. Vowels were correctly
produced in 83% (range: 38–100%) of the phonemic
paraphasias while the correct consonants were only
preserved in 31% (0–75%) of the cases.
Finally, we compared performance obtained with songs
and verbatim speech. Song lyrics were always better produced
than verbatim speech, in both sung and spoken repetition,
Z = 2.52 and 2.03, P < 0.05, respectively, and there was a
trend in that direction in spoken recall, Z = 1.68, P = 0.09.
The musical notes were also more accurate when sung with
the original lyrics than when paired with verbatim speech,
Z = 2.52, P < 0.05.
Discussion
Singing did not improve word production compared with
speaking. Even in songs where lyrics are usually sung, there
was no advantage of singing the words over speaking them.
This was also true for verbatim speech, such as proverbs and
prayers, which were not better repeated when sung on a
familiar melody than when spoken.
Table 6 Percentages of correct word and note
production for verbatim speech
Patient Repetition Recall
Combined Isolated Isolated
LB
Words 28 0 7
Notes 77
PP
Words 18 9 0
Notes 75
JS
Words 0 11 28
Notes 22
JH
Words 59 78 48
Notes 77
CA
Words 50 72 83
Notes 38
RD
Words 50 84 71
Notes 27
RH
Words 84 82 48
Notes 84
LD
Words 76 84 51
Notes 55
Mean
Words 45.6 52.5 39.8
Notes 56.9
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Musical production was generally more preserved than
speech. Interestingly, the melody was best performed when
sung with the lyrics, except for one patient (LD). This result
supports the fact that melody and text of songs are tightly
associated in memory so that the words can facilitate
melodic performance. The fact that this association is not as
effective in the other direction, from melody to words, in
aphasics suggests that the association is asymmetrical.
Finally, words in songs are more easily produced than
words in prayers or proverbs. Different factors may account
for this advantage. Songs may be more familiar, by being
more often practised or heard, than prayers or proverbs.
This difference in frequency of occurrence would make
songs more accessible in memory than verbatim speech.
Songs are also stored in memory in a dual code, that is, in
a speech and a musical code (Samson and Zatorre, 1991).
The melody might act as an additional cue that facilitates
the word retrieval compared with prayers or proverbs.
Thus, the automatic status of the material does not seem to
account entirely for the advantage of song production over
spontaneous speech in aphasic patients.
Experiment 2: Novel song learning
In novel songs, the sung version bears no advantage over the
spoken version because they are both heard together for the
first time. If singing does indeed facilitate word production in
non-fluent aphasics, sung words should be better produced
than spoken words when learning novel songs. The goal of the
present experiment was to test this prediction with repetition
and recall of novel words and notes. Furthermore, the
putative influence of music at encoding words was
also examined separately from its output influence. Presentation
of the lines was either sung or spoken (with the melody in
the background, referred to as the ‘divided’ presentation).
Repetition was sung, spoken or sung on /la/ (see Table 8).
Material
Unfamiliar songs were chosen from the repertoire of Claude
Gauthier, a popular French–Canadian folk-singer, author
and composer. Four songs with few repetitions of words or
melodic lines were selected (see Table 7 for an example). The
115 words used in the songs had a mean frequency of 2650
per million, including function words, based on a French
lexical database (New et al., 2001): 76% were highly
frequent, with a frequency of usage >50 per million. Only
10% of the words had a low frequency, corresponding to
<15 per million. The musical notes (nine per line on
average) outnumbered words (six per line on average),
t(31) = 11.21, SE = 0.22, P < 0.001.
The song excerpts were considered to be ‘good’ songs, as
assessed by seven pilot participants who were unfamiliar with
the singer. The judges were presented with the song excerpts
in their original version and these were randomly mixed with
Table 7 Illustration of the adaptive learning procedure with two trials
Lyrics presented Lyrics repeated Lyrics to be recalled
1 Dans cette petite boıˆte vide
1 Dans cette petite boıˆte vide
1 Dans cette petite boıˆte vide
1 Dans cette petite boıˆte vide
2 Avec un ruban de velours
2 Avec un ruban de velours
2 Avec un ruban de velours
2 Avec un ruban de velours
1 Dans cette petite boıˆte vide
2 Avec un ruban de velours
3 Il y a tout mon cœur et mes rides
3 Il y a tout mon cœur et mes rides
3 Il y a tout mon cœur et mes rides
3 Il y a tout mon cœur et mes rides
1 Dans cette petite boıˆte vide
2 Avec un ruban de velours
3 Il y a tout mon cœur et mes rides
4 Mon sourire et tout mon amour
4 Mon sourire et tout mon amour
4 Mon sourire et tout mon amour
4 Mon sourire et tout mon amour
1 Dans cette petite boıˆte vide
2 Avec un ruban de velours
3 Il y a tout mon cœur et mes rides
4 Mon sourire et tout mon amour
Table 8 Modes of presentation and production in each
condition of Experiment 2
Presentation Repetition/recall
Sung Sung
Sung Spoken
Spoken–sung on /la/ Spoken
Spoken–sung on /la/ Sung on /la/
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excerpts of hit songs from the same folk-singer. Each excerpt
was presented twice, in a random order. For each song
excerpt, the judges rated its musicality, its simplicity and its
potential to be a hit, on three six-point scales where 1 meant
poor, and 6 excellent. Very similar ratings were obtained for
the hits and the experimental songs on each dimension (3.7
and 3.8 for musicality, 3.4 and 3.5 for simplicity and 3.2 and
3.4 for hit potential, with first and second ratings pooled
together), supporting the idea that the selected material
corresponded to well-formed songs.
In all songs, there was a one-to-one mapping between
syllables and tones, with each syllable coupled with a single
note. Lines respected the grouping preference rules proposed
by Lerdahl and Jackendoff (1983). The alignment between
the prosody of the text and the rhythm of the melody
conformed to the rules of French songs (Dell, 1989).
Regarding musical structure, the songs had a stable and
standard metre. All the songs were in the major mode. Even
if melodies were chosen for their diversity, the melody parts
were highly coherent within a song.
Eight four-line excerpts (two per song) were included for
the learning task (see Table 7). On average, an excerpt
contained 25 words (range: 21–27) and 34 notes (range: 28–
38). An additional four-line excerpt from an unfamiliar
choir song (mean of five words and nine notes per line) by
Johann Steuerlein served as a training song.
The four songs and the training song were produced a
capella (without instrumental accompaniment) by a female
singer, who learned the songs beforehand. The same singer
also sang each song on /la/ and pronounced the lyrics with a
natural intonation. The three versions of the same song
served to create two types of stimuli, the sung and ‘divided’
songs. The latter were created by coupling each spoken line
with its corresponding melody sung on /la/, in order to give
the spoken presentation of words the same musical context
as the sung presentation of words. In these divided songs,
the intensity of the melody was decreased by 32%, on
average, in order to make the spoken version intelligible. The
intelligibility of songs’ lyrics was equivalent in the sung and
the ‘divided’ songs.
The length of the original spoken version was about half
of the length of the sung version [M = 2.48 and 4.95 s per
line, respectively, t(31) = 13.44, P < 0.001]. Because the
divided condition combined both the spoken and the ‘sung
on /la/’ versions (M = 4.92 s per line), divided and sung
presentations had equivalent lengths. In the divided
condition, the shorter spoken line was placed in the middle
of the sung melody, so that it was preceded and followed by
equivalent durations of the melody.
Procedure
The practice song was learned before each condition. It
served to determine the number of attempts that each
participant needed to achieve his/her best repetition of a
line. Patients had always the same number of attempts to
repeat a line throughout the experiment. The number of
trials varied between two for JH, RD, RH, LD; three for LB,
JS, CA; and four for PP. Then, the patients heard the whole
excerpt to be learned. Then, (s)he had to repeat (as many
times as determined previously) one line at a time, following
the procedure shown in Table 7. Each time a line was added,
the patient had to recall them from the beginning, until all
four lines had been presented, repeated and recalled.
In the sung–sung condition, the patient listened to the
sung version of the lyrics and sang them back. In the sung–
spoken condition, the patient listened to the sung version of
the lines and repeated only the lyrics, by pronouncing them in
a natural way. In the divided–spoken condition, the
participant listened to the divided version of the lines and
repeated again only the lyrics. In the last condition, dividedsung
on /la/ the participant listened to the divided version
and repeated only the melody, on the syllable /la/ (see Table 8).
The patients learned one excerpt in each condition, for a total
of four different songs. The order of presentation of the
conditions that involved repetition of lyrics (sung–sung,
sung–spoken, divided–spoken) was counter-balanced across
participants. The divided-sung on /la/ condition was done
last. Examples of stimuli and patients’ productions are
available at www.brams.umontreal.ca/peretz/.
Data scoring was performed as in Experiment 1, the
best repetition performance and recall of the whole song
(lines 1–4) being analysed for words and notes. The best
Table 9 Percentage of words correctly reproduced in
Experiment 2
Patient Word production
Sung–sung Sung–spoken Divided–spoken
LB
Repetition 9 31 14
Recall 8 0 0
PP
Repetition 5 0 0
Recall 0 0 0
JS
Repetition 9 10 11
Recall 4 8 10
JH
Repetition 54 51 50
Recall 13 9 14
CA
Repetition 26 49 56
Recall 0 9 4
RD
Repetition 69 50 70
Recall 25 4 44
RH
Repetition 78 83 88
Recall 21 26 24
LD
Repetition 85 70 83
Recall 36 29 63
Mean
Repetition 41.9 43.0 46.5
Recall 13.4 10.6 19.9
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repetition for the sung production corresponded to the best
word repetition performance. The scoring system thus
favoured word production over musical note production in
the combined production.
Results
As can be seen in Tables 9 and 10, there was a large
variability in the scores obtained in each condition. Despite
this important variability, none of the patients obtained a
higher score in the sung–sung condition than in the spoken
conditions. Singing did not help word repetition. The
comparison of the three conditions involving words (sung–
sung, sung–spoken, divided–spoken; Table 9) did not reveal
any difference, x2
(2) = 1.23, n.s. (by Friedman test). Similar
effects were obtained with recall scores [x2
(2) = 2.74, n.s.; see
Table 9].
The rate of syllable production was calculated in order to
examine if singing slowed down aphasic’s speech relatively
to speaking. The rate corresponded to the total duration of
word articulation (independently of accuracy) divided by the
number of syllables produced. Sung syllables (M = 572 ms)
were slower than the spoken syllables (M = 494 ms), with Z
= 2.20, P < 0.05.
The types of word errors were similar across the three
conditions in repetition and there was no effect of condition
(all P > 0.05). Omissions were the most common type of
error (40%). As for familiar songs, phonemic paraphasias
were slightly more frequent in the sung condition (34%
versus 17 and 20% for the sung–spoken and the divided–
spoken conditions, respectively). Compared with university
students, who only made omissions and substitutions
(Racette and Peretz, 2006), aphasic patients made more
omissions and slightly less substitutions (semantic and
lexical paraphasias). Thus, phonemic paraphasias are specific
to aphasic patients; they preserved the syllabic structure of
the word in 85% of the cases, the vowels in 80% and the
consonants in only 37%.
As can be seen in Table 10, the patients did not reproduce
more musical notes when singing with words (sung–sung
condition) than when singing on /la/ (divided-sung on /la/
condition), Z = 1.61, n.s. and Z = 0, n.s., in repetition and
recall, respectively. The scores for notes repetition were
actually higher without than with words for six patients. The
proportions of correctly reproduced words and musical
notes were also compared and did not yield any significant
difference in the sung–sung condition, with Z = 0 and 0.67,
both n.s., in repetition and recall, respectively. The same was
true when notes and words were produced in isolation after
the divided presentation, Z = 0.14 and 1.19, both n.s., in
repetition and recall, respectively. There was no correlation
between word and note scores (all P > 0.05). As expected,
recall scores were always inferior to repetition scores, in each
condition and for each component (all P < 0.05).
Discussion
Music did not improve intelligibility or verbal memory
when aphasics learned a novel song. Moreover, the aphasic
patients made the same type of errors when singing and
when speaking, suggesting that the speech output, whether
sung or spoken, was controlled by the same mechanisms.
Music had no effect on presentation. Hearing a sung text (in
the sung–spoken condition) did not lead to better word
production than hearing the spoken text (in the divided–
spoken condition). Thus, the results obtained here with
unfamiliar songs parallel those found with familiar songs in
Experiment 1.
Unlike Experiment 1, the aphasics did not reproduce
more notes than words. However, here, the scoring system
was biased towards words in the combined productions of
words and notes to enable comparison across conditions. In
our prior study with students, they performed much better
on the verbal than musical component (Racette and Peretz,
2006). Here, in aphasics, the superiority of words over notes
in production was reduced.
Experiment 3: Production in unison
In this last experiment, we examined another strategy that is
often used in speech therapy, particularly the MIT, which
consists in singing in unison with the therapist before trying
to sing alone. We examined here how the patients benefit
from singing and speaking along. Repetition and recall of
Table 10 Percentage of notes correctly reproduced in
Experiment 2
Patient Note production
Sung–sung Divided-sung on /la/
LB
Repetition 28 89
Recall 6 58
PP
Repetition 37 33
Recall 0 4
JS
Repetition 43 69
Recall 0 0
JH
Repetition 60 67
Recall 56 11
CA
Repetition 30 34
Recall 0 0
RD
Repetition 17 50
Recall 11 39
RH
Repetition 70 48
Recall 21 16
LD
Repetition 15 23
Recall 28 0
Mean
Repetition 37.5 51.6
Recall 15.3 16.0
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words and notes were done in unison. We also used two
speeds of presentation—the original and a much slower
one—to further assess the role of rate of articulation.
Method
Only the sung–sung and the divided–spoken conditions were
used here at the same speed as used in Experiment 2. These
versions were also slowed down by 50%, using the Cool Edit
program (Syntrillium Software Corporation, 2000). This reduction
of speed was chosen because it preserved the naturalness
of the voice. During repetition and recall, the patients
produced the words while listening to the target song lines.
Otherwise, the same learning procedure as used in Experiment
2 was followed. The sung–sung and divided–spoken
conditions were administered in the same order as used in
Experiment 2 for a given patient. Two excerpts (four lines)
were learned in each condition: one at the original speed
first and then at the slow speed, and vice versa for the other.
The speed was counter-balanced across conditions. A total of
four excerpts, different from the ones learned in Experiment
2, were learned: two while singing and two while speaking.
The patients listened to the songs via a headset that was
equipped with a microphone (see Fig. 1). The presented lines
fed one channel of a DAT Sony recorder and were timelocked
to the patient’s production that was recorded on the
other channel of the DAT recorder. Patients could hear
themselves but the headphones might have altered the
auditory–vocal feedback to some extent. Like in the previous
experiments, the best repetition was analysed.
Results and comments
Two conditions (sung–sung, divided–spoken) and two
speeds (original, slow) could be compared in repetition
and recall. This time, singing was found to improve
production. More words were correctly articulated when
singing along (M = 63.2%) than when speaking along (M =
50.5%), with Z = 2.10, P < 0.05 for repetition scores
averaged over the two speeds (see Table 11). Six of the eight
patients (LB, PP, JS, CA, RD, LD) showed this effect in
repetition and/or recall.
As for recall, before shadowing all the lines from the
beginning, the patients were first invited to recall words
alone, without the model. Patients’ performance could thus
be compared when recalling alone and along (see Table 11).
In general, recall from the beginning of the song was better
when shadowing than when producing alone without a
model, both when singing, Z = 2.52, P < 0.05, and when
speaking, Z = 2.24, P < 0.05. Shadowing was also beneficial
to pitch accuracy: repetition of musical notes was better in
unison than alone, Z = 2.38, P < 0.05 (see Table 12). Yet,
shadowing had a differential impact on singing and
speaking. Singing tended to be superior to speaking when
shadowing the four lines (with 46.2 versus 34.9%, for sung
and spoken recall along the model, respectively; Z = 1.82,
P = 0.07), as described above for each line. In contrast,
speaking tended to be more advantageous than singing when
recalling the four lines without shadowing (with 22 versus
8.1%, for spoken and sung recall alone, respectively; Z =
1.86, P = 0.06). Thus, singing alone does not facilitate word
recall, only singing along does.
The rate of articulation was significantly slower in singing
(696 ms/syllable) than in speaking (426 ms/syllable), with
Z = 2.52, P < 0.05. In order to explore the possible
Fig. 1 Setting used in Experiment 3.
Table 11 Percentages of words correctly reproduced in
Experiment 3 (shadowing-like task)
Patient Words
Original speed Slow speed
Sung–
sung
Divided–
spoken
Sung–
sung
Divided–
spoken
LB
Repetition 62 26 57 44
Recall 41 22 60 30
Alone 13 10 10 8
PP
Repetition 41 16 55 25
Recall 38 7 38 18
Alone 0 0 0 0
JS
Repetition 7 0 29 1
Recall 13 1 22 0
Alone 3 3 3 2
JH
Repetition 83 76 81 84
Recall 64 55 70 74
Alone 26 38 12 38
CA
Repetition 75 52 75 59
Recall 52 36 59 35
Alone 10 14 4 21
RD
Repetition 68 30 65 58
Recall 34 27 41 44
Alone 18 27 2 36
RH
Repetition 59 79 76 84
Recall 22 37 39 35
Alone 25 32 23 40
LD
Repetition 85 88 93 85
Recall 67 54 78 70
Alone 0 61 0 21
Mean
Repetition 60.0 46.3 66.4 55.0
Recall 41.4 31.4 50.9 38.3
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contribution of speed of articulation to the benefit of
singing, the individual rates of articulation, obtained in each
condition, were plotted as a function of the word score. This
plot is presented in Fig. 2. As can be seen, four of the six
patients (LB, PP, CA, RD) exhibited a significant correlation
between syllable duration and word accuracy. However,
most patients had small score differences in word accuracy
between the slow and the original speeds in singing (with
66.4 versus 60%, on average; Z = 1.35, n.s.), even though the
rates of articulation were significantly different between the
two presentation speeds (Z = 2.52, P < 0.05, with M = 796
and 596 ms/syllable for the slow and original speed,
respectively). In speaking, the effect of speed reaches
significance (with 55 versus 45.9%; Z = 2.24, P < 0.05)
and parallels the difference in rate of articulation (Z = 2.39,
P < 0.05, with M = 459 and 393 ms/syllable for the slow and
original speed, respectively). Thus, rate of articulation does
contribute to intelligibility of aphasics’ speech but cannot
fully account for the superiority of singing over speaking.
Moreover, in Experiment 2, patients were already producing
sung syllables at a slower speed than spoken syllables,
without exhibiting an advantage in word accuracy. Thus, the
advantage of slowing down rate of articulation on speech
production is neither constant nor general.
General discussion
The main finding from the present study is that singing
per se does not help aphasics to improve their speech. When
patients were producing words alone, without shadowing,
singing did not facilitate word articulation whether the songs
were familiar (Experiment 1) or unfamiliar (Experiment 2).
The similarity in the proportion of intelligible words and in
the types of error committed while singing and speaking
suggests that there is a unique code for words, either sung or
0
10
20
30
40
50
60
70
80
90
100
0 200 400 600 800 1000 1200
SYLLABLE DURATION (ms)
ACCURACYSCORE(%)
LB
PP
JS
CA
JH
RD
RH
LD
Fig. 2 Word accuracy according to syllable duration for each patient
in the spoken–original speed, the spoken–slow speed, the sung–
original speed and the sung–slow speed conditions in Experiment 3.
Table 12 Percentage of notes correctly reproduced in
Experiment 3 (shadowing-like task)
Patient Notes
Original speed Slow speed
LB
Repetition 81 76
Recall 80 87
PP
Repetition 86 100
Recall 89 100
JS
Repetition 62 69
Recall 56 65
JH
Repetition 56 66
Recall 55 52
CA
Repetition 76 80
Recall 64 66
RD
Repetition 49 40
Recall 34 45
RH
Repetition 83 89
Recall 37 55
LD
Repetition 79 87
Recall 61 77
Mean
Repetition 71.5 75.9
Recall 59.5 68.4
Table 13 Summary of the main results for word production in the three experiments
Experiment 1 Experiment 2 Experiment 3
Material(s) Familiar songs Unfamiliar songs Unfamiliar songs
Verbatim speech
Tasks Repetition Repetition Unison repetition
Recall Recall Recall alone
Unison recall
Conditions Sung Sung–sung Sung–slow speed
Spoken Sung–spoken Sung–original speed
Divided–spoken Spoken–slow speed
Spoken–original speed
Results Sung = spoken Sung = spoken Sung > spoken (in unison)
Sung = divided Spoken > sung (in recall alone)
Songs > verbatim speech Slow speed > original speed
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spoken. These results are consistent with two prior single case
studies in which patients were suffering from atypical aphasia
(He´bert et al., 2003; Peretz et al., 2004a) in showing that sung
repetition is no better than spoken repetition. The present
study extends this conclusion to a variety of speech disorders
caused by a left-hemisphere lesion because each patient
showed the same profile, at different levels of performance.
The novelty of the present study resides in the observation
in Experiment 3 that singing with someone else is better
than speaking in unison (see Table 13). Thus, singing has
more potential than speaking to improve intelligibility
when shadowing. Different factors may account for this
‘singing along’ advantage. As discussed earlier, the slower
rate of articulation imposed by singing is not sufficient to
account for this effect. In Experiment 2, rate of articulation
was slower in singing than in speaking, and yet, there was
no advantage of singing over speaking. In Experiment 3, a
decrease in rate of articulation was experimentally induced
by slowing down the speed of the model but it had little
impact on performance.
The ‘singing along’ advantage probably arises from the
opportunity to synchronize one’s performance with a stable
model. Vocal imitation alone is not sufficient to improve
speech intelligibility, because vocal imitation after a model
(via repetition) was involved in all three experiments. Even in
the presence of visual cues provided by face-to-face vocal
repetition, as was the case in Experiment 1 with familiar songs,
patients did not improve their speech while singing. What
seems to be critical is to be able to imitate in synchrony with an
auditory model. The physical presence of the model is not
necessary because the conditions of auditory–vocal synchrony,
or ‘choral singing’, that we used in Experiment 3 were limited
to the auditory channel. Moreover, the reduced memory load
that the shadowing task affords applied to both singing and
speaking in unison. Yet, shadowing only improved word
production in singing along. Thus, on-line singing imitation is
the critical condition to improve aphasic speech.
In singing along, the perception of the song is
synchronous to the production of that same song. This
principle can be related to the operation of mirror neurons
(Rizzolatti and Arbib, 1998; Iacoboni et al., 1999) or of an
auditory–motor interface (Hickok et al., 2003; Warren et al.,
2005; Callan et al., 2006). Both models posit a direct link
between perception and action for planning and performance.
According to these auditory–motor integration
models, there is a transformation of the auditory signal to a
form that constrains its articulatory output via templatematching
processes (Warren et al., 2005). This mechanism
would operate in language (Rizzolatti and Arbib, 1998;
Hickok et al., 2003) and in music (Callan et al., 2006). Thus,
in theory, this auditory–vocal interface (or mirror neurons
system) could contribute to the learning of novel songs as
tested here in aphasic patients, especially when singing at
the same time as an auditory model.
In principle, the auditory–vocal interface would intervene
in speaking, not only in singing. Indeed, choral speech
has been shown to improve speech intelligibility in people
who stutter (Kalinowski and Saltuklaroglu, 2003). Yet, we
observed a benefit of synchronous vocal imitation in singing
only. There are different factors that may account for this
advantage of choral singing. First, all of the aphasic
participants suffered from a left sylvian stroke that affected
the brain areas that are involved in the auditory–vocal
matching system in humans. That is, the lesions are located
in the vicinity of the left temporal region that is normally
active in both perception and covert production of sung and
spoken lyrics (Callan et al., 2006) and in both perception
and covert production of novel sentences and melodies,
as revealed by neuroimaging studies of normal subjects
(Hickok et al., 2003; Warren et al., 2005). Therefore, the
lesion may have damaged the auditory–vocal interfaces that
are involved in both speech and music (Hickok et al., 2003).
However, these systems are not totally overlapping.
For instance, the greater activation of the right planum
temporale for sung over spoken perception and production
of songs may reflect the contribution of a distinct auditory–
vocal interface recruiting the right side of the brain (Callan
et al., 2006). Right-lateralized brain regions are generally
more activated by singing than by speaking (Riecker et al.,
2000; Jeffries et al., 2003; Brown et al., 2006). Thus, the leftsided
lesions that produced the observed speech disorders
in our aphasic patients might have spared to some extent
the auditory–vocal interface involved in singing. Indeed,
all of our aphasic patients sang (without words) with
normal proficiency. Thus, it is reasonable to assume that the
auditory–vocal matching system involved in singing was
relatively spared in the aphasic patients. Hence, this
preserved auditory–vocal interface may account for the
advantage of choral singing over choral speaking.
This musical auditory–vocal interface seems to best
operate when singing is synchronous to a sung model.
Both word and pitch accuracy were higher in singing along
as compared with singing alone. Temporal accuracy could
not be assessed here but timing is, in all likelihood, a major
contributing factor because temporal regularity promotes
synchrony (Large and Palmer, 2002) and because sung lyrics
might be more regular than spoken lyrics, although this is
currently an open issue (Patel et al., 2006). In this respect, it
is worth noting that the tested patients were tested in
French. French is not a stress-based language while English
is. Future studies should test whether the advantage of
choral singing over choral speech is maintained in Englishspeaking
aphasic patients. Finally, choral singing is more
natural than choral speech. Participating in choir or singing
at church are familiar and highly enjoyable activities.
The present findings point to choral singing as an effective
therapy for various speech disorders. Choral singing may
even account for the efficacy of the MIT in its initial stages,
when patients sing in unison with the therapist. What might
happen in subsequent stages was not assessed in the present
study. Recent evidence suggests that singing is beneficial to
speech production in the long term (Wilson et al., 2006).
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Future longitudinal studies should compare training with
choral speech and choral singing to explore if the advantage
of choral singing has more long-lasting effects on speech
recovery.
More generally, we propose to use musical therapies in
treatments of aphasic patients because there are additional
benefits that may only be provided by music. Singing is a
natural and pleasurable way to entrain and engage someone.
Producing musical notes is also highly motivating because
it provides a natural way to express oneself vocally. As
mentioned previously, most patients had a profile of aphasia
without amusia (Brust, 2001; Warren et al., 2003). The
patients who had a concomitant musical impairment in
perception, memory or production did not show a different
vocal pattern than the patients without amusia. Hence,
singing can be used to motivate aphasic patients to engage in
lengthy and laborious sessions by keeping their spirit high.
Acknowledgements
We wish to thank all participants for their generous
participation and the AQPA for its help in finding
participants. We also wish to thank Yannick Ge´rard, Julie
Savaria and Annie Lavoie for their work in word and note
scoring, and Brigitte Damien and Luce Gosselin for the
language assessments. We thank Claude Gauthier for giving
us access to his song material, and Sylvie He´bert and Bernard
Bouchard for their assistance in creating the stimuli and in
melody transcription, respectively. Thanks to Pierre Ahad
for the setting in Experiment 3 and to Rene´e Be´land
for helpful comments along the way. This research was
supported by a CIHR doctoral fellowship to A.R. and grants
from the International Human Frontier Science Program
and the Natural Sciences and Engineering Research Council
of Canada to I.P.
References
Albert ML, Sparks RW, Helm NA. Melodic intonation therapy for aphasia.
Arch Neurol 1973; 29: 130–1.
Assal G, Buttet J, Javet RC. Aptitudes musicales chez les aphasiques. Rev Med
Suisse Romande 1977; 97: 5–12.
Belin P, Van Eeckhout P, Zilbovicius M, Remy P, Francois C, Guillaume S,
et al. Recovery from non-fluent aphasia after melodic intonation therapy:
a PET study. Neurology 1996; 47: 1504–11.
Blank SC, Scott SK, Murphy K, Warburton E, Wise RJS. Speech production:
Wernicke, Broca and beyond. Brain 2002; 125: 1829–38.
Boucher V, Garcia LJ, Fleurant J, Paradis J. Variable efficacy of rhythm and
tone in melody-based interventions: implications for the assumption of
a right-hemisphere facilitation in non-fluent aphasia. Aphasiology 2001;
15: 131–49.
Brown S, Martinez M, Parsons L. Music and language side by side in the brain:
a PET study of the generation of melodies and sentences. Eur J Neurosci
2006; 23: 2791–803.
Brust JCM. Music and the neurologist: a historical perspective. In: Zatorre RJ,
Peretz I, editors. The biological foundations of music. New York: The
New York Academy of Sciences; 2001: 143–52.
Burgio F, Basso A. Memory and aphasia. Neuropsychologia 1997; 35: 759–66.
Callan DE, Tsytsarev V, Hanakawa T, Callan AM, Katsuhara M, Fukuyama H,
et al. Song and speech: brain regions involved with perception and covert
production. Neuroimage 2006; 31: 1327–42.
Cohen NS, Ford J. The effects of musical cues on the nonpurposive speech of
persons with aphasia. J Music Ther 1995; 32: 46–57.
Crowder RG, Serafine ML, Repp B. Physical interaction and association by
contiguity in memory for the words and melodies of songs. Mem Cognit
1990; 18: 469–76.
Dell F. Concordances rythmiques entre la musique et les paroles dans le
chant. L’accent de l’e muet dans la chanson fran¸caise. In: Dominicy M,
editor. Le souci des apparences. Bruxelles: Universite´ de Bruxelles; 1989:
121–36.
De Renzi E, Vignolo L. The token test: a sensitive test to detect receptive
disturbances in aphasics. Brain 1962; 85: 665–78.
He´bert S, Racette A, Gagnon L, Peretz I. Revisiting the dissociation between
singing and speaking in expressive aphasia. Brain 2003; 126: 1838–50.
Hickok G, Buchsbaum B, Humphries C, Muftuler Y. Auditory-motor
interaction revealed by fMRI: speech, music, and working memory in area
Spt. J Cogn Neurosci 2003; 15: 673–82.
Hustad KC, Jones T, Dailey S. Implementing speech supplementation
strategies: effects on intelligibility and speech rate of individuals with
chronic severe dysarthria. J Speech Lang Hear Res 2003; 46: 462–74.
Iacoboni M, Woods RP, Brass M, Bekkering H, Mazziotta JC,
Rizzolatti G. Cortical mechanisms of human imitation. Science 1999;
286: 2526–8.
Jacome DE. Aphasia with elation, hypermusia, musicophilia and compulsive
whistling. J Neurol Neurosurg Psychiatry 1984; 47: 308–10.
Jeffries KJ, Fritz JB, Braun AR. Words in melody: an H(2)15O PET study
of brain activation during singing and speaking. Neuroreport 2003;
14: 749–54.
Kalinowski J, Saltuklaroglu T. Choral speech: the amelioration of stuttering
via imitation and the mirror neuronal system. Neurosci Biobehav Rev 2003;
27: 339–47.
Keith R, Aronson A. Singing as therapy for apraxia of speech and aphasia:
report of a case. Brain Lang 1975; 2: 482–8.
Kilgour AR, Jakobson LS, Cuddy LL. Music training and rate of presentation
as mediators of text and song recall. Mem Cognit 2000; 28: 700–10.
Langdon D, Warrington EK. The role of the left hemisphere in verbal and
spatial reasoning tasks. Cortex 2000; 36: 691–702.
Large EW, Palmer C. Perceiving temporal regularity in music. Cogn Sci 2002;
26: 1–37.
Laughlin SA, Naeser MA, Gordon WP. Effects of three syllable durations
using the melodic intonation therapy technique. J Speech Hear Res 1979;
22: 311–20.
Lerdahl F, Jackendoff R. A generative theory of tonal music. Cambridge, MA:
MIT Press; 1983.
Marin OSM, Perry DW. Neurological aspects of music perception and
performance. In: Deutsch D, editor. The psychology of music. New York:
Academic Press; 1999: 653–724.
Mazaux JM, Orgogozo JM. HDAE-F. Boston diagnostic aphasia examination.
Issy les Moulineaux: E´ditions Scientifiques et Psychologiques; 1981.
Naeser MA, Helm-Estabrooks N. CT scan localization and response to
melodic intonation therapy with non-fluent aphasia cases. Cortex 1985;
21: 203–23.
Nespoulous JL, Lecours AR, Lafond D. MT-86-Protocole Montre´al-Toulouse
d’examen linguistique de l’aphasie. Isberges: Ortho-Edition; 1992.
New B, Pallier C, Ferrand L, Matos R. Une base de donne´es lexicales du
fran¸cais contemporain sur internet: LEXIQUE. Anne´e Psychol 2001;
101: 447–62.
Patel AD, Iversen JR, Rosenberg JC. Comparing the rhythm and melody of
speech and music: the case of British English and French. J Acoust Soc Am
2006; 119: 3034–47.
Peretz I, Babai M, Lussier I, He´bert S, Gagnon L. Corpus d’extraits musicaux:
indices relatifs a` la familiarite´, a` l’aˆge d’acquisition et aux e´vocations
verbales. Can J Exp Psychol 1995; 49: 211–39.
Peretz I, Champod AS, Hyde K. Varieties of musical disorders. The Montreal
Battery of Evaluation of Amusia. Ann NY Acad Sci 2003; 999: 58–75.
Peretz I, Gagnon L, Macoir J, He´bert S. Singing in the brain: insights from
cognitive neuropsychology. Music Percept 2004a; 21: 373–90.
Making non-fluent aphasics sing Brain (2006), 129, 2571–2584 2583
byguestonFebruary12,2014http://brain.oxfordjournals.org/Downloadedfrom
Peretz I, Radeau M, Arguin M. Two-way interactions between music and
language: evidence from priming recognition of tune and lyrics in familiar
songs. Mem Cognit 2004b; 32: 142–52.
Pilon MA, McIntosh KW, Thaut MH. Auditory vs visual speech timing
cues as external rate control to enhance verbal intelligibility in mixed
spastic-ataxic dysarthric speakers: a pilot study. Brain Inj 1998; 12: 793–803.
Poulin-Charronnat B, Bigand E, Madurell F, Peereman R. Musical structure
modulates semantic priming in vocal music. Cognition 2005; 94: B67–B78.
Racette A, Peretz I. Learning lyrics: to sing or not to sing? Mem Cognit 2006.
In press.
Raven JC. Standard progressive matrices: sets A, B, C, D & E. Oxford, UK:
Oxford Psychologists Press; 1996.
Riecker A, Ackermann H, Wildgruber D, Dogil G, Grodd W. Opposite
hemispheric lateralization effects during speaking and singing at motor
cortex, insula and cerebellum. Neuroreport 2000; 11: 1997–2000.
Rizzolatti G, Arbib MA. Language within our grasp. Trends Neurosci 1988;
21: 188–94.
Rubin DC. Memory in oral traditions. New York: Oxford University Press;
1995.
Ryding E, Bradvik B, Ingvar DH. Changes of regional cerebral blood flow
measured simultaneously in the right and left hemisphere during automatic
speech and humming. Brain 1987; 110: 1345–58.
Saltuklaroglu T, Kalinowski J, Guntupalli VK. Towards a common neural
substrate in the immediate and effective inhibition of stuttering. Int J
Neurosci 2004; 114: 435–50.
Samson S, Zatorre R. Recognition memory for text and melody of songs after
unilateral temporal lobe lesion: evidence for dual encoding. J Exp Psychol
Learn Mem Cogn 1991; 17: 193–804.
Serafine ML, Crowder RG, Repp BH. Integration of melody and text in
memory for songs. Cognition 1984; 16: 285–303.
Serafine ML, Davidson RJ, Crowder RG, Repp BH. On the nature of melody.
Text integration in memory for songs. J Mem Lang 1986; 25: 123–35.
Shallice T. Specific impairments of planning. Philos Trans R Soc Lond 1982;
298: 199–209.
Sparks R, Helm N, Albert M. Aphasia rehabilitation resulting from melodic
intonation therapy. Cortex 1974; 10: 303–16.
Speedie LJ, Wertman E, Ta’ir J, Heilman KM. Disruption of automatic speech
following a right basal ganglia lesion. Neurology 1993; 43: 1768–74.
Van Lancker-Sidtis D. When novel sentences spoken or heard for the first time
in the history of the universe are not enough: toward a dual-process model
of language. Int J Lang Commun Disord 2004; 39: 1–44.
Wallace WT. Memory for music: effect of melody on recall of text. J Exp
Psychol Learn Mem Cogn 1994; 20: 1471–85.
Warren JD, Warren JE, Fox NC, Warrington EK. Nothing to say, something
to sing: primary progressive dynamic aphasia. Neurocase 2003;
9: 140–55.
Warren JE, Wise RJS, Warren JD. Sounds do-able; auditory-motor transformations
and the posterior temporal plane. Trends Neurosci 28: 636–43.
Wilson S, Parsons K, Reutens DC. Preserved singing in aphasia: a case study
of the efficacy of melodic intonation therapy. Music Perception 2006.
In press.
Yamadori A, Osumi Y, Masuhara S, Okubo M. Preservation of
singing in Broca’s aphasia. J Neurol Neurosurg Psychiatry 1977;
40: 221–4.
Yorkston KM, Beukelman DR. Communication efficiency of dysarthric
speakers as measured by sentence intelligibility and speaking rate. J Speech
Hear Disord 1981; 46: 296–301.
Yorkston KM, Hammen VL, Beukelman DR, Traynor CD. The effect of rate
control on the intelligibility and naturalness of dysarthric speech. J Speech
Hear Disord 1990; 55: 550–60.
2584 Brain (2006), 129, 2571–2584 A. Racette et al.
byguestonFebruary12,2014http://brain.oxfordjournals.org/Downloadedfrom