A Self Learning Vocal Interface for Speech-impaired Users
Bart Ons, Netsanet M. Tessema, Janneke van de Loo, Jort Florent Gemmeke, Guy De Pauw, Walter M. P. Daelemans, Hugo Van hamme · Lirias · 2013
In this work we describe research aimed at developing an assis-tive vocal interface for users with a speech impairment. In con-trast to existing approaches, the vocal interface is self-learning, which means it is maximally adapted to the end-user and can be used with any language, dialect, vocabulary and grammar. The paper describes the overall learning framework and the vocabulary acquisition technique, and proposes a novel gram-mar induction technique based on weakly supervised hidden Markov model learning. We evaluate early implementations of these vocabulary and grammar learning components on two datasets: recorded sessions of a vocally guided card game by non-impaired speakers and speech-impaired users engaging in a home automation task. Index Terms: vocal user interface, self-taught learning, dysarthric speech, non negative matrix factorization, hidden