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1: Classification of 8 languages along rhythmic dimensions %V and ∆C. Figure reproduced from Ramus, Nespor, and Mehler (1999).

1: Classification of 8 languages along rhythmic dimensions %V and ∆C. Figure reproduced from Ramus, Nespor, and Mehler (1999).

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During the first years of life, children rapidly learn to process speech from a continuous acoustic signal, and soon become able to understand and produce the sounds, words and structure of their native language. Children growing up in a bilingual environment face an additional challenge: they must simultaneously discover and separate their bilingu...

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... samples were thus manually segmented into CV sequences, and for each utterance three different measures capturing the proportion of V segments (%V ) and the standard deviation of V and C segments (∆V & ∆C) were computed. Their results showed that %V combined with ∆C resulted in an apparent clustering of the languages (shown in Figure 2.1) into rhythmic classes. ...
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... this section we will briefly describe the pipeline of the i-vector model (shown in Figure 2.2). For a detailed description of each step, see the Appendix of the present chapter (Section 2.A). ...
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... -English 60% Figure 2.3: PCA visualization of utterances produced by 4 bilingual speakers from the UCAM corpus. ...
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... question is of great importance to hypothesize whether infants would be able to easily group their bilingual input by language and not by speaker. In Figure 2.4 we visualize the first two PCA dimensions of the i-vectors of all 4 language pairs. ...
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... in previous experiments, we first computed a PCA on the i-vectors for each language pair in order to visualize the data. Figure 2.6 shows the first two dimensions of the PCA for each pair. As can be seen in the visualization, all language pairs appear to show a higher degree of overlap compared to the unfiltered speech, suggesting that the model was relying to some extent on phonological differences. ...
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... results are shown in Table 2.3. Figure 2.6: PCA visualization of low-pass filtered utterances produced by 4 bilingual speakers from the UCAM corpus. ...
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... in , for each language pair available in the corpus, we simulated an experiment with 40 subjects (20 control and 20 experimental). An example of a simulated experiment is given in Figure 2.7a. interpreted the p-value of control vs. experimental conditions as an indicator of the discriminability of each language pair, with p < 0.05 indicating a significant difference between both groups (thus implying discrimination of the language pair), and p ≥ 0.05 meaning no significant group difference was found (i.e., no language discrimination). ...
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... the p-value of control vs. experimental conditions as an indicator of the discriminability of each language pair, with p < 0.05 indicating a significant difference between both groups (thus implying discrimination of the language pair), and p ≥ 0.05 meaning no significant group difference was found (i.e., no language discrimination). When running our replication, we first observed that repeating the simulation of 40 subjects several times sometimes changed the interpretation of the discriminability for certain pairs, with p-values going from significant to non-significant (see an example of a stable language pair, Dutch-Japanese, and a language pair with variable p-values, Dutch-English, in Figure 2.7b). These unstable results may be due to the large number of possible utterance sets, and to the fact that we did not control them for duration and syllable length. ...
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... we repeated the simulations using unfiltered speech. Figure 2.9 shows the matrix of results. ...
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... computed the ABX score for each of the 4 language pairs using the i-vectors extracted from the four new models. Figure 2.11 shows a comparison of all the models, including the one from Experiment 2. As can be seen, the results obtained for the three French background models are very similar, indicating that the models have learnt similar features from their input despite having been exposed to different speakers. ...
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... speaker-based LDA rescues the mixed background models from being utterly confused. Finally, we applied several clustering methods to the test i-vectors. For each of the three background conditions, we computed clustering purity as a function of the number of clusters in each level of the hierarchical tree using eight different linkage methods. Fig. 2 shows the results before and after LDA for a complete linkage clustering 1 from K = 1 to K = ...
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... Figure 2.12, we show a visualization of the old post-LDA utterances (from Experiment 6) and the new utterances, projected onto the same LDA dimensions learnt in Experiment 6. ...
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... many different sets of features can be used for this purpose, here we will use the popular MFCC-SDC features which, as discussed before, can capture both static and dynamic information. For every step in the feature extraction process (shown in Figure 2.13) we will discuss, when possible, the parallels between the algorithms and human auditory perception. ...
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... resulting MFCCs are D-dimensional feature vectors that capture short-term spectral information that is relevant for speech recognition. By repeating this process over every frame along the signal, any given utterance can be translated into an MFCC sequence or matrix, as shown in Figure 2.19. ...
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... algorithm is initialized by setting arbitrary weights, means and covariances ω 0 k , µ 0 k , Σ 0 k for each component (see Figure 2.22, Step 0). ...
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... whose children learnt French from a nanny or at daycare), and finally only 2 had one parent who spoke both languages equally often. We then examined differences in the way parents used the languages with the child and with other people. Figure 3.2 shows a histogram of the languages used by fathers and mothers when addressing their infants (Fig. 3.2a) and when talking to other people in front of the child (Fig. 3.2b) as reported in the LEQ. As can be seen in the figure on the left, in spite of generally adhering to an OPOL division of languages, some parents reported also speaking a small amount of the other language when talking to their child (cases mostly-FR, mostly-L2 ). This ...
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... finally only 2 had one parent who spoke both languages equally often. We then examined differences in the way parents used the languages with the child and with other people. Figure 3.2 shows a histogram of the languages used by fathers and mothers when addressing their infants (Fig. 3.2a) and when talking to other people in front of the child (Fig. 3.2b) as reported in the LEQ. As can be seen in the figure on the left, in spite of generally adhering to an OPOL division of languages, some parents reported also speaking a small amount of the other language when talking to their child (cases mostly-FR, mostly-L2 ). This behavior was more common in L2 speakers than in French speakers, ...
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... responses were automatically collected by the app and coded as a binary dependent variable representing whether the child clicked on the picture or the cross in each trial. Figure 2 shows the proportion of trials where the picture of the familiar object was chosen, split by condition. Responses were analyzed with a generalized linear mixed model (GLMM) with binomial family and logit link using package lme4 Table 2. ...

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... The results of discrimination tasks with young infants depend on the naturalness of the speech stimuli as well as on talker variability in the stimulus set. Recent computational work, testing the contribution of sub-lexical cues (acoustic/phonetic, phonotactic, and prosodic) to language discrimination (Carbajal, 2018) suggests higher discriminability with natural than with filtered speech. An i-vector model trained on 25 min of speech (French in most experiments) representing a listening experience of a 5-days old infant (Carbajal, 2018, p. 66) yielded higher discriminability with natural stimuli than with low-pass filtered stimuli, and with singlespeaker stimuli compared to multiple speakers. ...
... Thus, in line with Carbajal's (2018) simulations, empirical studies confirm a single-talker advantage over multiple-talker stimuli in early language discrimination and confirm the natural speech over synthetic speech advantage in older infants. These developmental trends might be explained be the infants' growing experience with the ambient language(s). ...
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This article reviews empirical methods and findings on early language discrimination, questioning rhythm‐class based hypotheses on language discrimination in infancy, as well as the assumption that early language discrimination is driven primarily (or solely) by temporal prosodic cues. The present work argues that within‐rhythm class discrimination which – according to the rhythmic hypothesis – is not applicable very early in life, has not been sufficiently tested with infants under 4 months of age, that familiarity with a language is not a prerequisite for its discrimination from another rhythmically similar language, and that the temporal rhythm properties may not universally be the primary cues to language discrimination. Although rhythm taxonomy is now by many understood as outdated, some developmental literature still draws on the assumption that rhythm classification determines infants' language discrimination; other studies consider rhythm along a continuous scale and only a few account for cues to language discrimination other than temporal ones. It is proposed that studies on early language discrimination systematically test the contribution of other than temporal rhythm cues, similarly to recent work on multidimensional psychoacoustic salience in the acquisition of segmental categories.
... I-vectors based systems have been shown to reproduce key findings in language discrimination experiments: the ability to detect a change in language within a bilingual speaker (language discrimination) (Carbajal, Dawud, Thiollière, & Dupoux, 2016), the distance effect between different language pairs, with close languages being harder to discriminate than more distant languages (Carbajal, 2018), and the ability to discriminate based on prosody (Martinez, Lleida, Ortega, & Miguel, 2013;Carbajal, 2018). However, they also resulted in an intriguing prediction that has not so far been verified experimentally. ...
... I-vectors based systems have been shown to reproduce key findings in language discrimination experiments: the ability to detect a change in language within a bilingual speaker (language discrimination) (Carbajal, Dawud, Thiollière, & Dupoux, 2016), the distance effect between different language pairs, with close languages being harder to discriminate than more distant languages (Carbajal, 2018), and the ability to discriminate based on prosody (Martinez, Lleida, Ortega, & Miguel, 2013;Carbajal, 2018). However, they also resulted in an intriguing prediction that has not so far been verified experimentally. ...
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The language discrimination process in infants has been successfully modeled using i-vector based systems, with results replicating several experimental findings. Still, recent work found intriguing results regarding the difference between monolingual and mixed-language exposure on language discrimination tasks. We use two carefully designed datasets, with an additional "bilingual" condition on the i-vector model of language discrimination. Our results do not show any difference in the ability of discriminating languages between the three backgrounds, although we do replicate past observations that distant languages (English-Finnish) are easier to discriminate than close languages (English-German). We do, however, find a strong effect of background when testing for the ability of the learner to automatically sort sentences in language clusters: bilingual background being generally harder than mixed background (one speaker one language). Other analyses reveal that clustering is dominated by speakers information rather than by languages.
Chapter
Bilingualism and the study of speech sounds are two of the largest areas of inquiry in linguistics. This Handbook sits at the intersection of these fields, providing a comprehensive overview of the most recent, cutting-edge work on the sound systems of adult and child bilinguals. Bringing together contributions from an international team of world-leading experts, it covers all aspects of the speech perception, production and processing of bilingual individuals, as well as surveying cross-linguistic influences on the phonetics and phonology of bilingualism. The thirty-five chapters are divided into thematic areas covering the theoretical foundations and methodological approaches employed to investigate bilingual speech, overviews of major findings and developments in child and adult bilingual phonology and phonetics, descriptions of the major areas of research within the speech perception, production and processing of the bilingual individual, and examinations of various predictors of cross-linguistic influence and variables affecting the outcomes of bilingual speech.