Title: Speaker adaptation method for HMM-based speech recognition
Abstract: The authors describe a speaker adaptation method consisting of two stages. In the first stage, label prototypes, which represent spectral features, are modified to reduce the total distortion error of vector quantization for a new speaker. In the second stage, well-trained hidden Markov model (HMM) parameters are transformed by using a linear mapping function. This is estimated by counting the correspondences along the alignment between a state sequence of an HMM and a label sequence of a new speaker utterance. This adaptation procedure was tested in an isolated word recognition task using 150 confusable Japanese words. The original label prototypes and HMM parameters were estimated for a male speaker, who spoke each word 10 times. When the adaptation procedure was applied with 25 words, the average error rate for another seven male speakers was reduced from 25.0% to 5.6%, which was roughly the same as that for the original speaker. This procedure was also effective for adaptation between male and female speakers.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Publication Year: 2003
Publication Date: 2003-01-06
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 16
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot