Signal conditioning techniques for robust speech recognition

Abstract

Acoustic mismatch encountered in various training and testing conditions of hidden Markov model (HMM) based systems often causes severe degradation in speech recognition performance. For telephone based speech recognition tasks, acoustic mismatch can arise from various sources, such as variations in telephone handsets, ambient noise, and channel distortions. This paper presents three techniques for blind channel equalization, namely, cepstral mean subtraction (CMS), signal bias removal (SBR) and hierarchical signal bias removal (HSBR). Experimental results on various connected digits databases show a reduction in the digit error rate by 16%, 21%, and 28% when employing CMS, SBR, and HSBR, respectively. Our results also demonstrate that the HSBR technique outperforms SBR and CMS on every sub-data collection and exhibits consistent improvements even for short utterances.