David J. Ostry, Jean-Luc Schwartz, Jean-François Patri, Christophe Savariaux, Julien Diard, Pascal Perrier, Pamela Trudeau-Fisette, GIPSA - Perception, Contrôle, Multimodalité et Dynamiques de la parole (GIPSA-PCMD), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Cognition, Motion and Neuroscience Unit, Fondazione Istituto Italiano di Tecnologia, McGill University = Université McGill [Montréal, Canada], Laboratoire de Psychologie et NeuroCognition (LPNC ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Laboratoire de Phonétique, Laboratoire de Phonétique [Montréal], Université du Québec à Montréal = University of Québec in Montréal (UQAM)-Université du Québec à Montréal = University of Québec in Montréal (UQAM), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA-Services (GIPSA-Services), European Research Council under the European Community’s Seventh Framework Program (FP7/2007-2 013 grant agreement 339152, 'Speech Unit(e)s', principal investigator, Jean-Luc Schwartz)The European Union’s Horizon 2020 research and innovation program under the Marie Skłokodowska-Curie grant agreement 754490 (MultIscale precision therapies for NeuroDEvelopmental Disorders [MINDED] Program: Beneficiairy: Jean-François Patri),The National Institute on Deafness and Other Communication Disorders grant R01DC017439 (PI: David J. Ostry). NeuroCoG 'IDEX Université Grenoble Alpes:- Université de l’innovation' in the framework of the 'Investissements d’avenir' program (ANR-15-IDEX-02)., Gipsa-lab - LPNC - Psychology Department, MCGill University, Montréal, Canada -Haskins Laboratories, New Haven,USA - Lab Phonétique, UQAM, Montréal, Canada - Fondazione Istituto Italiano di Tecnologia, Genova, Italy, ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), and European Project: 339152,EC:FP7:ERC,ERC-2013-ADG,SPEECH UNIT(E)S(2014)
Auditory speech perception enables listeners to access phonological categories from speech sounds. During speech production and speech motor learning, speakers’ experience matched auditory and somatosensory input. Accordingly, access to phonetic units might also be provided by somatosensory information. The present study assessed whether humans can identify vowels using somatosensory feedback, without auditory feedback. A tongue-positioning task was used in which participants were required to achieve different tongue postures within the /e, [Formula: see text] , a/ articulatory range, in a procedure that was totally nonspeech like, involving distorted visual feedback of tongue shape. Tongue postures were measured using electromagnetic articulography. At the end of each tongue-positioning trial, subjects were required to whisper the corresponding vocal tract configuration with masked auditory feedback and to identify the vowel associated with the reached tongue posture. Masked auditory feedback ensured that vowel categorization was based on somatosensory feedback rather than auditory feedback. A separate group of subjects was required to auditorily classify the whispered sounds. In addition, we modeled the link between vowel categories and tongue postures in normal speech production with a Bayesian classifier based on the tongue postures recorded from the same speakers for several repetitions of the /e, [Formula: see text] , a/ vowels during a separate speech production task. Overall, our results indicate that vowel categorization is possible with somatosensory feedback alone, with an accuracy that is similar to the accuracy of the auditory perception of whispered sounds, and in congruence with normal speech articulation, as accounted for by the Bayesian classifier.