7th International Conference on Spoken Language Processing

September 16-20, 2002
Denver, Colorado, USA

Unsupervised N-Best Based Model Adaptation Using Model-Level Confidence Measures

Ka-Yan Kwan, Tan Lee, Chen Yang

Chinese University of Hong Kong, China

This paper presents a study on using confidence measures for unsupervised N-Best based adaptation of hidden Markov model (HMM) parameters. Confidence measures have been widely used for the detection of speech recognition errors. They are also useful in selecting and/or screening data for unsupervised adaptation of HMM. In this paper, a model-level confidence measure is proposed for model adaptation with the Maximum Likelihood Linear Regression (MLLR) technique. The model-level confidence measure provides a finer selection of adaptation data than the word or utterance level measures. The proposed confidence measure is derived from the N-best hypotheses. The computation involves not only the recognized models but also other models that are easily confused with them. Experimental results show the proposed confidence measure improves the effectiveness of unsupervised model adaptation. The relative improvement in word error rate is up to 9.75%.

Full Paper

Bibliographic reference.  Kwan, Ka-Yan / Lee, Tan / Yang, Chen (2002): "Unsupervised n-best based model adaptation using model-level confidence measures", In ICSLP-2002, 69-72.