Second International Conference on Spoken Language Processing (ICSLP'92)

Banff, Alberta, Canada
October 13-16, 1992

Connectionist Gender Adaptation in a Hybrid Neural Network / Hidden Markov Model Speech Recognition System

Victor Abrash (1), Horacio Franco (1), Michael Cohen (1), Nelson Morgan (2), Yochai Konig (2)

(1) Speech Research Program, SRI International, Menlo Park, CA, USA (2) Intl. Computer Science Inst., Berkeley, CA, USA

An approach to modeling long-term consistencies in a speech signal within the framework of a hybrid Hidden Markov Model (HMM) / Multilayer Perception (MLP) speaker-independent continuous-speech recognition system is presented. Several ways to model male and female speech more accurately with separate models are discussed, one of which is investigated in depth. A method which combines gender-independent and -dependent MLP training is demonstrated, improving recognition accuracy while retaining robustness. A series of network architectures (using our training method) for the connectionist estimation of gender-dependent HMM observation probabilities are evaluated in terms of recognition performance and number of additional parameters needed. Experimental evalutation shows a significant improvement in word recognition accuracy over the gender-independent system with a moderate increase in the number of parameters.

Full Paper

Bibliographic reference.  Abrash, Victor / Franco, Horacio / Cohen, Michael / Morgan, Nelson / Konig, Yochai (1992): "Connectionist gender adaptation in a hybrid neural network / hidden Markov model speech recognition system", In ICSLP-1992, 911-914.