L’antériorisation de /ɔ/ en français contemporain : une étude acoustique comparative entre Québec et France

La présente étude porte sur l’antériorisation de /ɔ/, souvent étudiée en France, mais relativement peu au Québec. Pour mieux explorer la variation diatopique liée à ce phénomène et clarifier sa relation avec le contexte consonantique, une analyse acoustique comparative des trois premiers formants de 3835 voyelles produites en position accentuée dans des mots et pseudo-mots monosyllabiques (C)VC par 78 étudiants universitaires de Saguenay et de Québec (Québec) et de Lyon (France) a été menée. Des modèles de régression linéaire à effets mixtes appliqués aux données permettent de constater une différence importante du F2 de /ɔ/ entre les villes, cette voyelle étant plus antérieure à Québec qu’à Saguenay et à Lyon qu’à Saguenay. Les voyelles québécoises et françaises se distinguent également, dans une moindre mesure, sur le plan de F1 et de F3. Dans tous les cas, quelle que soit leur position (antéposée ou postposée), les consonnes antérieures (ex. /t, d/) favorisent le F2 le plus élevé et les consonnes labiales (ex. /p, b/), le F2 le plus bas. Ces résultats indiquent que l’antériorisation de /ɔ/ à l’échelle acoustique est bien présente chez les jeunes locuteurs québécois et qu’elle est variable tant au niveau micro-géographique que macro-géographique, tout en étant largement affectée par la coarticulation chez tous les locuteurs. This study is concerned with /ɔ/-fronting, a phenomenon widely studied in France, but that has received little attention in Quebec French. To better understand the regional variation of /ɔ/-fronting and to further investigate its relationship with the consonantal environment, a contrastive acoustic analysis of the first three formants of 3835 stressed vowels uttered in (C)VC monosyllabic word and pseudo-words by university students from Saguenay and Québec (Quebec) and Lyon (France) was conducted. Linear mixed effects regressions fitted to the data show substantial variation in F2 across cities, /ɔ/ being more fronted in Québec than in Saguenay and in Lyon than in Saguenay. F1 and F3 also vary between France and Quebec French. In all cases, no matter their position (before or after the vowel), front consonants (ex. /t, d/) favor the highest F2, while labial consonants (ex. /p, b/) are associated with the lowest F2. These results suggest that /ɔ/-fronting, at the acoustical level, is alive and well among young Quebec French speakers and that it is variable both at the micro- and macrogeographical levels, while also being largely affected by coarticulation across all speakers

L'antériorisation de /ɔ/ en français contemporain : une étude acoustique comparative entre Québec et France

Protocole d'entente entre l'Université Laval et l'Université du Québec à ChicoutimiLa présente étude porte sur l’antériorisation de /ɔ/, souvent étudiée en France, mais relativement peu au Québec. Pour mieux explorer la variation diatopique liée à ce phénomène et clarifier sa relation avec le contexte consonantique, une analyse acoustique comparative des trois premiers formants de 3835 voyelles produites en position accentuée dans des mots et pseudo-mots monosyllabiques (C)VC par 78 étudiants universitaires de Saguenay et de Québec (Québec) et de Lyon (France) a été menée. Des modèles de régression linéaire à effets mixtes appliqués aux données permettent de constater une différence importante du F₂ de /ɔ/ entre les villes, cette voyelle étant plus antérieure à Québec qu’à Saguenay et à Lyon qu’à Saguenay. Les voyelles québécoises et françaises se distinguent également, dans une moindre mesure, sur le plan de F₁ et de F₃. Dans tous les cas, quelle que soit leur position (antéposée ou postposée), les consonnes antérieures (ex. /t, d/) favorisent le F₂ le plus élevé et les consonnes labiales (ex. /p, b/), le F₂ le plus bas. Ces résultats indiquent que l’antériorisation de /ɔ/ à l’échelle acoustique est bien présente chez les jeunes locuteurs québécois et qu’elle est variable tant au niveau micro-géographique que macro-géographique, tout en étant largement affectée par la coarticulation chez tous les locuteurs.This study is concerned with /ɔ/-fronting, a phenomenon widely studied in France, but that has received little attention in Quebec French. To better understand the regional variation of /ɔ/-fronting and to further investigate its relationship with the consonantal environment, a contrastive acoustic analysis of the first three formants of 3835 stressed vowels uttered in (C)VC monosyllabic word and pseudo-words by university students from Saguenay and Québec (Quebec) and Lyon (France) was conducted. Linear mixed effects regressions fitted to the data show substantial variation in F₂ across cities, /ɔ/ being more fronted in Québec than in Saguenay and in Lyon than in Saguenay. F₁ and F₃ also vary between France and Quebec French. In all cases, no matter their position (before or after the vowel), front consonants (ex. /t, d/) favor the highest F₂, while labial consonants (ex. /p, b/) are associated with the lowest F₂. These results suggest that /ɔ/-fronting, at the acoustical level, is alive and well among young Quebec French speakers and that it is variable both at the micro- and macro-geographical levels, while also being largely affected by coarticulation across all speakers

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls.

Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy

Controlling for call types in the individual signature task.

Controlling for call types in the individual signature task.</p

Representation of call f₀ templates at the individual level.

Each color/hue combination corresponds to a call type (P, Y, PY, SB, B, SCB, as defined in Fig 3). Each curve is a miniature of an individual’s f0 template. The call type (acronym and color) and individual identity (numerical index) are indicated. All individuals and call types for which at least 3 samples were available are displayed. The repertoire of individuals #19 and #20 is highlighted (thick lines).</p

Examples of spectrograms of the five call types.

Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy.</div

Raw dataset.

Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy.</div

Influence of the sampling on data leakage (sequences with at least three calls considered).

Three scenarios are applied: Default, Fair and Skewed. Left. Distribution of the 100 runs for each strategy in terms of sequence overlap between training and test sets (0: no overlap). Right. Influence of strategy on performance (balanced accuracy) for each combination of classifiers and acoustic feature sets when classifying individual signatures.</p

Projections of bonobo calls into bidimensional acoustic spaces through S-UMAP computed on the raw acoustic features of the Bioacoustic, DCT, and MFCC sets (1,560 calls; each dot = 1 call; different colors encode different hand-labeled categories).

Left. Top. S-UMAP projection supervised by call types. Bottom. Silhouette profiles corresponding to the call type clustering, built from a 100-repetition distribution of silhouette scores, with averages and standard deviations per call type being represented by dashed vertical and horizontal lines, respectively. Right. Top. S-UMAP projection supervised by individual identities. Bottom. Silhouette profiles corresponding to the individual signature clustering, built from a 100-repetition distribution of silhouette scores, with averages and standard deviations per individual being represented by dashed vertical and horizontal lines, respectively.</p

Metrics characterizing the classification performance of individual signatures as a function of the classifier and acoustic set used.

Four metrics are reported: log loss, AUC, balanced accuracy, and accuracy. The best performance achieved by a primary configuration (upper part) and an ensemble configuration (lower part) is displayed in bold. For AUC, accuracy (acc) and balanced accuracy (bac), a color scale highlights the progression from the lowest scores (in pale orange) to the highest scores (in dark orange) in the column.</p