title: Computational validation of the motor contribution to speech perception.
abstract: Action perception and recognition are core abilities fundamental for human social interaction. A parieto-frontal network (the mirror neuron system) matches visually presented biological motion information onto observers' motor representations. This process of matching the actions of others onto our own sensorimotor repertoire is thought to be important for action recognition, providing a non-mediated "motor perception" based on a bidirectional flow of information along the mirror parieto-frontal circuits. State-of-the-art machine learning strategies for hand action identification have shown better performances when sensorimotor data, as opposed to visual information only, are available during learning. As speech is a particular type of action (with acoustic targets), it is expected to activate a mirror neuron mechanism. Indeed, in speech perception, motor centers have been shown to be causally involved in the discrimination of speech sounds. In this paper, we review recent neurophysiological and machine learning-based studies showing (a) the specific contribution of the motor system to speech perception and (b) that automatic phone recognition is significantly improved when motor data are used during training of classifiers (as opposed to learning from purely auditory data).
authors.affiliation: RBCS - Robotics, Brain and Cognitive Sciences Department, IIT - Istituto Italiano di Tecnologia.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Badino
authors.forename: Alessandro
authors.initials: A
authors.lastname: D'Ausilio
authors.forename: Luciano
authors.initials: L
authors.lastname: Fadiga
authors.forename: Giorgio
authors.initials: G
authors.lastname: Metta
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D010363:Pattern Recognition, Automated; D011597:Psychomotor Performance / Q000502:physiology*; D021641:Recognition, Psychology / Q000502:physiology; D013067:Speech Perception / Q000502:physiology*
keywords: Automatic speech recognition; Machine learning; Motor theory of speech perception; Transcranial magnetic stimulation