A Study of the Automatic Speech Recognition Process and Speaker Adaptation
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Date
2000
Authors
Stokes-Rees, Ian James
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
This thesis considers the entire automated speech recognition process and presents a standardised approach to LVCSR experimentation with HMMs. It also discusses various approaches to speaker adaptation such as MLLR and multiscale, and presents experimental results for crossÂ-task speaker adaptation. An analysis of training parameters and data sufficiency for reasonable system performance estimates are also included. It is found that Maximum Likelihood Linear Regression (MLLR) supervised adaptation can result in 6% reduction (absolute) in word error rate given only one minute of adaptation data, as compared with an unadapted model set trained on a different task. The unadapted system performed at 24% WER and the adapted system at 18% WER. This is achieved with only 4 to 7 adaptation classes per speaker, as generated from a regression tree.
Description
Keywords
Electrical & Computer Engineering, automatic speech recognition, speaker adaptation, HTK, HMM, MLLR, LVCSR