Recent neurocognitive models commonly consider speech perception as a hierarchy of processes, each corresponding to specific temporal scales of collective oscillatory processes in the cortex: 30–80 Hz gamma oscillations in charge of phonetic analysis, 4–9 Hz theta oscillations in charge of syllabic segmentation, 1–2 Hz delta oscillations processing prosodic/syntactic units and the 15–20 Hz beta channel possibly involved in top-down predictions. Several recent neuro-computational models thus feature theta oscillations, driven by the speech acoustic envelope, to achieve syllabic parsing before lexical access. However, it is unlikely that such syllabic parsing, performed in a purely bottom-up manner from envelope variations, would be totally efficient in all situations, especially in adverse sensory conditions. We present a new probabilistic model of spoken word recognition, called COSMO-Onset, in which syllabic parsing relies on fusion between top-down, lexical prediction of onset events and bottom-up onset detection from the acoustic envelope. We report preliminary simulations, analyzing how the model performs syllabic parsing and phone, syllable and word recognition. We show that, while purely bottom-up onset detection is sufficient for word recognition in nominal conditions, top-down prediction of syllabic onset events allows overcoming challenging adverse conditions, such as when the acoustic envelope is degraded, leading either to spurious or missing onset events in the sensory signal. This provides a proposal for a possible computational functional role of top-down, predictive processes during speech recognition, consistent with recent models of neuronal oscillatory processes.

Nous avons développé un cadre de modélisation des processus de la communication parlée, COSMO (« Communicating Objects by Sensory-Motor Operations »), qui s'applique à la fois aux modèles de perception et de production de parole en installant les relations sensori-motrices au coeur de son programme. COSMO permet de formaliser conjointement deux cadres théoriques majeurs des recherches sur la communication parlée, les théories auditives et motrices -mais aussi de les intégrer au sein de théories perceptuo-motrices. Ceci conduit ainsi à de nouveaux modèles de perception alliant traitements auditifs et prise en compte de connaissances motrices, ou de nouveaux modèles de contrôle moteur de la parole orientés vers la réalisation de buts sensoriels multimodaux. Nous présentons ces avancées ainsi que des pistes de développement sur le traitement temporel et l'implémentation deep learning permettant d'aller vers l'apprentissage sur des données réelles.