Robust ASR inside a Vehicle Using Blind Probabilistic Based Under-Determined Convolutive Mixture Separation Technique

Abstract

Spoken dialogue based information retrieval systems are being used in mobile environments such as cars. However, the car environment is noisy and the user’s speech signal gets corrupted due to dynamically changing acoustic environment and the number of interference signals inside the car. The interference signals get mixed with speech signals convolutively due to the chamber impulse response. This tends to degrade the performance of a speech recognition system which is an integral part of a spoken dialogue based information retrieval system. One solution to alleviate this problem is to enhance speech signals such that the recognition accuracy does not degrade much. In this Chapter, we describe a blind source separation technique that would enhance convolutively mixed speech signals by separating the interference signals from the genuine speech. This technique is applicable for under-determined case i.e., the number of microphones is less than the number of signal sources and uses a probabilistic approach in a sparse transformed domain. We have collected speech data inside a car with variable number of interference sources such as wipers on, radio on, A/C on. We have applied our blind convolutive mixture separation technique to enhance the mixed speech signals. We conducted experiments to obtain speech recognition accuracy using with and without enhanced speech signals. For these experiments we used a continuous speech recognizer. Our results indicate 15–35 % improvement in speech recognition accuracy.