Title :
A confidence-score based unsupervised MAP adaptation for speech recognition
Author :
Wang, Dagen ; Narayanan, Shrikanth S.
Author_Institution :
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
In this paper, a method of confidence-score based MAP (maximum a posteriori) adaptation in speech recognition is proposed and evaluated. Using confidence scores to dynamically decide the weight of the priors is shown to have good performance improvement in an unsupervised incremental adaptation. The side effect of vocabulary mismatch in adaptation is also effectively controlled by this way. This paper first gives theoretical analysis and then shows some experimental results. Several extensions are made and also discussed.
Keywords :
maximum likelihood estimation; speech recognition; ASR; MAP; MLLR; automatic speech recognition; confidence score; maximum a posteriori adaptation; maximum likelihood linear regression adaptation; speech adaptation; vocabulary mismatch; Adaptation model; Automatic control; Automatic speech recognition; Automatic testing; Decoding; Hidden Markov models; Maximum likelihood linear regression; Probability; Speech recognition; Vocabulary;
Conference_Titel :
Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-7576-9
DOI :
10.1109/ACSSC.2002.1197181
