資源簡介
Hidden Markov Models (HMMs) provide a simple and effective framework
for modelling time-varying spectral vector sequences. As a consequence,
almost all present day large vocabulary continuous speech
recognition (LVCSR) systems are based on HMMs.
Whereas the basic principles underlying HMM-based LVCSR are
rather straightforward, the approximations and simplifying assumptions
involved in a direct implementation of these principles would
result in a system which has poor accuracy and unacceptable sensitivity
to changes in operating environment. Thus, the practical
application of HMMs in modern systems involves considerable
sophistication.
The aim of this review is first to present the core architecture of
a HMM-based LVCSR system and then describe the various refinements
which are needed to achieve state-of-the-art performance. These
refinements include feature projection, improved covariance modelling,
discriminative parameter estimation, adaptation and normalisation,
noise compensation and multi-pass system combination. The review
concludes with a case study of LVCSR for Broadcast News and
Conversation transcription in order to illustrate the techniques
described.
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