Results and discussion

II.2.4.1 Duration

We calculated the mean cluster duration for each condition (schwa cluster, underlying cluster, pseudoword cluster). The cluster is shortest in the schwa condition (127 ms, sd = 34), longest in the pseudoword condition (152 ms, sd = 36) and intermediate for the underlying condition (133 ms, sd = 34). Figure 8 shows the clusters’ mean durations and standard errors for each condition and Figure 9 shows the mean duration for each condition, as a function of cluster. As can be seen in this figure, the duration of the cluster in the three conditions shows considerable variation among clusters.

Figure 8. Mean cluster duration (ms) in each condition (schwa cluster, underlying cluster in pseudoword and underlying cluster in word) in Phonetic

Study 1. The bars represent the standard errors.

Cluster type 0

20 40 60 80 100 120 140 160 180

Schwa Pseudoword Underlying

Mean duration (ms)

65 Figure 9. Mean cluster duration in the three conditions (P.: pseudoword cluster,

S.: schwa cluster, U.: underlying cluster) as a function of cluster in Phonetic study 1.

In the present and subsequent studies, we analyzed the data by means of mixed-effects models (see Goldstein, 1987, 1995; Rasbash & Goldstein, 1994 and Baayen, Davidson &

Bates, 2008, for details on mixed-effects applied to psycholinguistic data). While these statistical methods are not new, it is only recently that they have been made available through their implementation in statistical softwares such as R (R Development Core Team, 2007; or Mlwin, http://www.cmm.bristol.ac.uk/MlwiN /index.shtml). In addition to the standard fixed effects considered in simple linear regression modeling, mixed-effects models can account for the random variation induced for instance by the given words, word categories (e.g., clusters in the present analysis) or speakers. These models are more precise in their predictions and more powerful than most traditional analyses, while still being

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In order to examine whether cluster mean duration differs as a function of condition, we ran a mixed-effects model with speaker and cluster as random terms, cluster duration as the dependent variable and condition as the only predictor (or fixed effect). Following Baayen (2008), residuals larger than 2.5 times the standard deviation (25 data points forming 1.8%

of the data) were considered outliers and removed.

The model shows an effect of condition F(2,1394) = 126.04, p < 0.0001. Each contrast is significant after the Bonferroni¹¹ correction is applied; the mean cluster duration is longer in the pseudoword condition than in the underlying and schwa conditions (p < 0.0001) and the mean cluster duration is longer in the underlying than in the schwa condition (p < 0.001).

In order to test the significance of the random terms, we compared our final model with two simpler models, each identical to the final model but including one random effect only (either for speaker or for cluster), by means of likelihood-ratio tests (see Baayen, 2008, p.

253). These comparisons confirmed the necessity of including the two random terms (speaker: χ²(1) = 423.8, p < 0.0001, cluster: χ² (1) = 210.1, p < 0.0001) in the statistical model.

In the next analysis, we examined whether these differences in duration were best explained by condition (schwa, underlying and pseudoword) or by other predictors known to affect segment duration. We again ran a mixed-effects model with cluster and speaker as random terms and cluster duration as the dependent variable. We added several new predictors to our previous model: number of phonemes, number of syllables, speech rate and lexical frequency. As the number of syllables and the number of phonemes were highly correlated (r

= 0.86), they were orthogonalized by replacing the variable Number of syllables by the residuals of a linear model in which the number of phonemes predicted the number of syllables. As in all our analyses with mixed-effects models, we then used a stepwise procedure; predictors were entered sequentially in the model, and retained only if significant (at p < 0.05). Thus the contribution of each predictor is determined once the contribution of the previous predictors is taken into account.

11 The Bonferroni correction is applied when multiple comparisons are conducted on the same data set (Baayen, 2008) to prevent type I errors (i.e., finding a significant difference where there is none). The Bonferroni corrrection consists of dividing α by the number of comparisons. For a factor with three levels (as this is the case for instance for the factor “condition” in our study), three comparisons are conducted, hence α must be divided by 3. Only comparisons whose p values are below 0.05/3 (i.e., 0.0167) will thus be considered significant.

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Residuals larger than 2.5 times the standard deviation (31 data points forming 2.2% of the data) were considered outliers and removed. Including random terms for speaker and for cluster significantly improved the final model according to likelihood-ratio tests (speaker:

χ²(1) = 161.0, p < 0.0001, cluster: χ² (1) = 252.4, p < 0.0001). This new model shows four main effects (see Table 2 for statistical values). Cluster duration decreases with lexical frequency, word length (number of phonemes) and speech rate and is influenced by condition. Pseudowords have again a longer cluster duration than underlying and schwa clusters but there is no difference between schwa and underlying clusters. Figure 10 shows the partial effects of this model (i.e., the effect of each predictor while other predictors are held constant).

Table 2. Summary of the mixed-effects model for cluster duration in Phonetic study 1.

Variable β F P

Lexical frequency - 0.057 11.59 <0.001

Number of phonemes -10.00 247.56 <0.0001

Speech rate -16740 510.07 <0.0001

Condition

Pseudowords vs. Schwa Pseudowords vs. underlying

Schwa vs. underlying

13.30 10.28 3.02

29.07 <0.0001

<0.0001

<0.0001

>0.1

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Figure 10. Partial effects as predicted by the statistical model for schwa duration (n = 1422) in Phonetic study 1.

We can conclude from these data that there is no difference in cluster duration between the schwa and underlying conditions. Furthermore, our results show that clusters in pseudowords behave differently from underlying and schwa clusters.

II.2.4.2 Spectrographic analysis

As the set of acoustic cues differed for each cluster examined, we analyzed each cluster separately. For the cues which showed some variation in the responses between the conditions (i.e., the cue was present in some tokens and absent in others), a generalized mixed-effects model¹² with presence/absence of the cue as the response was run (e.g.,

12 Like linear mixed-effects models, generalized mixed-effects models account for fixed as well as random variation. They are used for binomially distributed outcomes. They allow us to define which predictors influence the probability of a given outcome (i.e., presence versus absence of the acoustic cue in the present analysis).

0 100 200 300 400 500

100140180

Lexical frequency

Cluster duration (ms)

3 4 5 6 7 8 9

100140180

Number of phonemes

Cluster duration (ms)

0.004 0.006 0.008

100140180

Speech rate

Cluster duration (ms) 100140180

Condition

Cluster duration (ms)

pseudow. schwa underl.

Jaeger, 2008; Quené & van den Bergh, 2008

condition as the fixed effect. For ease of presentation, results for these statistical analyses are omitted in the text but are reported in

The clusters [mn] and [lm] acoustic cue considered.

The clusters [pl] and [lv] only show differences between conditions. For the cluster

than schwa and underlying clusters. For the cluster a formant structure between the two consonants

the schwa and the pseudoword conditions for the cluster consonant of the cluster shows more devoicing for the item than for the item caplé (pseudoword condition).

Figure 11. Production

caplé (right) by the same speaker

Only in the four remaining clusters

between the schwa and the underlying conditions. For the

from underlying clusters but are similar to pseudowords. There are more tokens produced with an increase in amplitude of the s

clusters than in schwa and pseudoword clusters. An consonants could signal a residue of the schwa vowel.

observed more frequently i

suggestion. Figure 12 illustrates the increase in amplitude between the two consonants for Quené & van den Bergh, 2008). Speaker was entered as a random effect and condition as the fixed effect. For ease of presentation, results for these statistical analyses are omitted in the text but are reported in APPENDIX 3.

] show no difference between the three conditions

only show differences between the pseudowords and the other two . For the cluster [pl], pseudowords show less devoicing of the second consonant than schwa and underlying clusters. For the cluster [lv], pseudowords have fewer tokens with a formant structure between the two consonants. Figure 11 illustrates the diffe

the schwa and the pseudoword conditions for the cluster [pl]. As can be seen, the second consonant of the cluster shows more devoicing for the item appelé ‘call’ (schwa condition)

(pseudoword condition).

Productions of the schwa word appelé (left) and the pseudoword (right) by the same speaker in Phonetic study 1. The cluster [

indicated by the blue rectangles.

remaining clusters (i.e., [sl], [vn], [tn], and [f]) do we find differences between the schwa and the underlying conditions. For the [sl] cluster, schwa clusters differ from underlying clusters but are similar to pseudowords. There are more tokens produced with an increase in amplitude of the signal between the two consonants in underlying wa and pseudoword clusters. An increase in amplitude between the two consonants could signal a residue of the schwa vowel. However, the fact that this increase is observed more frequently in underlying clusters than in schwa clusters refutes this illustrates the increase in amplitude between the two consonants for

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Speaker was entered as a random effect and condition as the fixed effect. For ease of presentation, results for these statistical analyses are

e between the three conditions whatever the

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the sequence [sla] produced in the underlying condition (left) and its absence in the production of the same sequence for the schwa condition.

Figure 12. Productions of the sequence [ the schwa word cela

cluster [

Interestingly, the [vn] cluster shows the inverse pattern; there are more tokens with an increase in signal amplitude between the two consonants for schw

clusters, at the position where an underlying schwa could be assumed

For the clusters [f] and [tn], the differences between the schwa and underlying condition are again inconsistent with the view that schwa clusters contain acoust

schwa vowel. The observed differences rather suggest that the two consonants are more coarticulated/overlapped in the schwa condition compared to underlying clusters.

cluster [f], we find more tokens with a voiced right consonan involving the cluster [tn]. While the production of the schwa word

shows no burst for the [t] consonant, in the production of the word ethnie (right) there is a double burst for this same consonant.

] produced in the underlying condition (left) and its absence in the production of the same sequence for the schwa condition.

Productions of the sequence [sla] in the word slalom (left) and in a (right) by the same speaker in Phonetic study 1. The cluster [sl] is indicated by the blue rectangles.

cluster shows the inverse pattern; there are more tokens with an increase in signal amplitude between the two consonants for schwa than for underly

at the position where an underlying schwa could be assumed.

, the differences between the schwa and underlying condition inconsistent with the view that schwa clusters contain acoustic residues of the

The observed differences rather suggest that the two consonants are more coarticulated/overlapped in the schwa condition compared to underlying clusters.

], we find more tokens with a voiced right consonant for underlying

or the cluster [tn], there are more tokens realized with a burst, a double burst, a noisy/fricative release portion or a silent portion between the two consonants for underlying clusters than for schwa clusters. In addition, there are more tokens realized with complete silence between the two consonants for pseudoword clusters than for schwa

illustrates this difference with two productions of the same speaker ]. While the production of the schwa word contenir

] consonant, in the production of the word ethnie (right) there is a double burst for this same consonant.

, the differences between the schwa and underlying conditions ic residues of the The observed differences rather suggest that the two consonants are more coarticulated/overlapped in the schwa condition compared to underlying clusters. For the for underlying rather than ] consonant, in the production of the word ethnie (right) there is a

Figure 13. Productions in the word ethnie

According to this spectrographic ana

systematic difference between the schwa and between these two conditions for four

could be interpreted as a residue

II.2.4.3 Automatic acoustic analysis

For each group of clusters [

(Breiman, Friedman, Olshen & Stone, 1984

questions best minimize the average acoustical distance between all the members of each leaf of the tree. The criteria used for CART questions were speaker identity, condition (schwa / underlying / pseudoword), cluster duration and a random value.

The tree building algorithm only selected that list. We reported in Table

random value was used as the first criterion for all groups except [

three clusters, the first classification criterion is the pseudoword condition and the second one is the random value. Thus, for all clusters except [

underlying / pseudowords) has no s

clusters. For these three clusters, the pseudoword condition differs from the schwa and underlying conditions, but belonging to the schwa or underlying condition does not affect the acoustical distance between clusters.

Productions of the cluster [tn] in the schwa word contenir

ethnie (right) by the same speaker in Phonetic study 1. The cluster [tn] is indicated by the blue rectangles.

According to this spectrographic analysis, we can conclude that clusters

difference between the schwa and the underlying conditions. A difference exists two conditions for four clusters. However, for one cluster only

could be interpreted as a residue of a schwa vowel.

analysis

For each group of clusters [f], [lm], [lv], [mn], [pl], [sl], [tn] and [vn

Breiman, Friedman, Olshen & Stone, 1984) was used to build a decision tree whose questions best minimize the average acoustical distance between all the members of each criteria used for CART questions were speaker identity, condition (schwa / underlying / pseudoword), cluster duration and a random value.

building algorithm only selected the questions that were the most significant among Table 3 the first question used by the CART for each cluster. The random value was used as the first criterion for all groups except [lm], [lv

three clusters, the first classification criterion is the pseudoword condition and the second one is the random value. Thus, for all clusters except [lm], [lv] and [tn], cluster type (schwa / underlying / pseudowords) has no significant impact on the acoustical distance between clusters. For these three clusters, the pseudoword condition differs from the schwa and . However, for one cluster only this difference

vn], a CART method ) was used to build a decision tree whose questions best minimize the average acoustical distance between all the members of each criteria used for CART questions were speaker identity, condition (schwa / underlying / pseudoword), cluster duration and a random value.

the questions that were the most significant among the first question used by the CART for each cluster. The lv] and [tn]. For these three clusters, the first classification criterion is the pseudoword condition and the second ], cluster type (schwa / ignificant impact on the acoustical distance between clusters. For these three clusters, the pseudoword condition differs from the schwa and underlying conditions, but belonging to the schwa or underlying condition does not affect

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Table 3. First classification criteria for each cluster according to the automatic acoustic analysis in Phonetic study 1.

Cluster First classification criterion

f Random

lm Pseudoword

lv Pseudoword

mn Random

pl Random

sl Random

tn Pseudoword

vn Random

Results of this automatic analysis show that clusters from the schwa and underlying conditions do not differ acoustically. Furthermore, some clusters differ in pseudowords compared to the other two conditions. These results are thus in line with the results we obtained in the two previous analyses (duration and spectrographic analysis). They show little evidence that clusters in schwa words differ from identical underlying clusters.

Dans le document Phonological variation and lexical representation in speech production : phonetic and psycholinguistic evidence on french schwa (Page 77-85)