Classically, in the bouba-kiki association task, a subject is asked to find the best association between one of two shapes–a round one and a spiky one–and one of two pseudowords–bouba and kiki. Numerous studies report that spiky shapes are associated with kiki, and round shapes with bouba. This task is likely the most prevalent in the study of non-conventional relationships between linguistic forms and meanings, also known as sound symbolism. However, associative tasks are explicit in the sense that they highlight phonetic and visual contrasts and require subjects to establish a crossmodal link between stimuli of different natures. Additionally, recent studies have raised the question whether visual resemblances between the target shapes and the letters explain the pattern of association, at least in literate subjects. In this paper, we report a more implicit testing paradigm of the bouba-kiki effect with the use of a lexical decision task with character strings presented in round or spiky frames. Pseudowords and words are, furthermore, displayed with either an angular or a curvy font to investigate possible graphemic bias. Innovative analyses of response times are performed with GAMLSS models, which offer a large range of possible distributions of error terms, and a generalized Gama distribution is found to be the most appropriate. No sound symbolic effect appears to be significant, but an interaction effect is in particular observed between spiky shapes and angular letters leading to faster response times. We discuss these results with respect to the visual saliency of angular shapes, priming, brain activation, synaesthesia and ideasthesia.

L'antériorisation de /ɔ/, largement étudiée en France, n'a reçu que peu d'attention au Québec. Afin de documenter une éventuelle variation diatopique entre France et Québec, une analyse acoustique comparative de la fréquence centrale du deuxième formant (F 2) de 2837 voyelles produites dans des mots et non-mots monosyllabiques (C)VC par des étudiants universitaires de Saguenay (Québec) et de Lyon (France) a été menée. Un modèle de régression linéaire à effets mixtes appliqué aux données indique que /ɔ/ est significativement plus antérieur à Lyon qu'à Saguenay. Dans les deux villes, le lieu d'articulation de la consonne antéposée et celui de la consonne postposée influencent la structure acoustique de cette voyelle. Quelle que soit leur position, les consonnes antérieures (ex. /t, d/) favorisent le F 2 le plus élevé ; les consonnes labiales (ex. /p, b/), le F 2 le plus bas.

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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.