Combining top-down syllabic duration prediction with bottom-up envelope processing for syllabic segmentation in speech perception: a computational Modeling study with the COSMO-Onset model
Résumé
Recent neurocomputational speech perception models include a segmentation process delimiting speech segments, typically syllable-long, before decoding. This process is conceived as purely bottom-up, detecting temporally salient events in the speech signal. Beyond the scope of current models, the COSMO-Onset model incorporates in the segmentation process top-down predictions based on linguistic knowledge of speech rhythms. We present an adaptation of the model, previously studied on artificial stimuli, to process real speech signals. The model is used to simulate syllable-type recognition in noise, with two main results. Firstly, bottom-up segmentation based on resonant processes favours isochrony, since sentences with a more regular structure of syllabic events provide better segmentation and recognition scores. Secondly, top-down lexical predictions of syllable duration make detection and syllable recognition more robust in noise. The combination of bottom-up resonant and top-down predictive processes yields simulations in line with a recent behavioural experiment on speech comprehension in noise.
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