Fast vocabulary acquisition in an NMF-based self-learning vocal user interface

In command-and-control applications, a vocal user interface (VUI) is useful for handsfree control of various devices, especially or people with a physical disability. The spoken utterances are usually restricted to a predefined list of phrases or to a restricted rammar, and the acoustic models work well for normal speech. While some state-of-the-art methods allow for user adaptation of he predefined acoustic models and lexicons, we pursue a fully adaptive VUI by learning both vocabulary and acoustics directly rom interaction examples. A learning curve usually has a steep rise in the beginning and an asymptotic ceiling at the end. To imit tutoring time and to guarantee good performance in the long run, the word learning rate of the VUI should be fast and the earning curve should level off at a high accuracy. In order to deal with these performance indicators, we propose a multi-level UI architecture and we investigate the effectiveness of alternative processing schemes. In the low-level layer, we explore the use f MIDA features (Mutual Information Discrimination Analysis) against conventional MFCC features. In the mid-level layer, we nhance the acoustic representation by means of phone posteriorgrams and clustering procedures. In the high-level layer, we use the MF (Non-negative Matrix Factorization) procedure which has been demonstrated to be an effective approach for word learning. e evaluate and discuss the performance and the feasibility of our approach in a realistic experimental setting of the VUI-user earning context. 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/3.0/).