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Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/1274

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Title: Speaker-Adaptation for Hybrid HMM-ANN Continuous Speech Recognition System
Authors: Neto, Joao
Almeida, Luis
Hochberg, Mike
Martins, Ciro
Nunes, Luis
Renals, Steve
Robinson, Tony
Issue Date: 1995
Citation: 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.
Publisher: International Speech Communication Association
Abstract: 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.
URI: http://hdl.handle.net/1842/1274
Appears in Collections:CSTR thesis and dissertation collection

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