Exemplar-based Recognition of Speech in Highly Variable Noise

Robustness against varying background noise is a crucial requirement for the use of automatic speech recognition in everyday situations. In previous work, we proposed an exemplar-based recognition system for tackling the issue at low SNRs. In this work, we compare several exemplar-based factorisation and decoding algorithms in pursuit of higher noise robustness. The algorithms are evaluated using the PASCAL CHiME challenge corpus, which contains multiple speakers and authentic living room noise at six SNRs ranging from 9 to -6 dB. The results show that the proposed exemplar-based techniques offer a substantial improvement in the noise robustness of speech recognition.

automatic speech recognition; exemplar-based; noise robustness; sparse representation

• speech recognition