It is well known that recognition performance degrades significantly when moving from a speaker-dependent to a speaker-independent system. Traditional hidden Markov model (HMM) systems have successfully applied speaker-adaptation approaches to reduce this degradation. In this paper we present and evaluate some techniques for speaker-adaptation of a hybrid HMM-artificial neural network (ANN) continuous speech recognition system. These techniques are applied to a well trained, speaker-independent, hybrid HMM-ANN system and the recognizer parameters are adapted to a new speaker through off-line procedures. The techniques are evaluated on the DARPA RM corpus using varying amounts of adaptation material and different ANN architectures. The results show that speaker-adaptation within the hybrid framework can substantially improve system performance.
Bibliographic reference. Neto, Joao / Almeida, Luis / Hochberg, Mike / Martins, Ciro / Nunes, Luis / Renals, Steve / Robinson, Tony (1995): "Speaker-adaptation for hybrid HMM-ANN continuous speech recognition system", In EUROSPEECH-1995, 2171-2174.