September 22-25, 1997
In this paper, we present a family of maximum likelihood (ML) techniques that aim at reducing an acoustic mismatch between the training and testing conditions of hid- den Markov model (HMM)-based automatic speech recognition (ASR) systems. We propose a codebook-based stochastic matching (CBSM) approach for bias removal both at the feature level and at the model level. CBSM associates each bias with an ensemble of HMM mixture components that share similar acoustic characteristics. It is integrated with hierarchical signal bias removal (HSBR) and further extended to accommodate for N-best candidates. Experimental results on connected digits, recorded over a cellular network, shows that the proposed system reduces both the word and string error rates by about 36% and 31%, respectively, over a baseline system not incorporating bias removal.
Bibliographic reference. Lawrence, Craig / Rahim, Mazin (1997): "Integrated bias removal techniques for robust speech recognition \lambda", In EUROSPEECH-1997, 2567-2570.