Continuous Word Recognition Based on the Stochastic Segment Model (1992)
| Venue: | Proc. DARPA Workshop CSR |
| Citations: | 8 - 2 self |
BibTeX
@INPROCEEDINGS{Ostendorf92continuousword,
author = {Mari Ostendorf and Ashvin Kannan and Owen Kimball and J. Robin Rohlicek},
title = {Continuous Word Recognition Based on the Stochastic Segment Model},
booktitle = {Proc. DARPA Workshop CSR},
year = {1992},
pages = {53--58}
}
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Abstract
This paper presents an overview of the Boston University continuous word recognition system, which is based on the Stochastic Segment Model (SSM). The key components of the system described here include: a segment-based acoustic model that uses a family of Gaussian distributions to characterize variable length segments; a divisive clustering technique for estimating robust context-dependent models; and recognition using the N-best rescoring formalism, which also provides a mechanism for combining different knowledge sources (e.g. SSM and HMM scores). Results are reported for the speaker-independent portion of the Resource Management Corpus, for both the SSM system and a combined BU-SSM/BBN-HMM system. 1. INTRODUCTION In the last decade, most of the research on continuous speech recognition has focused on different variations of hidden Markov models (HMMs), and the various efforts have led to significant improvements in recognition performance. However, some researchers have begun to ...
