Publication
ICASSP 1999
Paper

Recent improvements to IBM's speech recognition system for automatic transcription of broadcast news

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Abstract

We describe recent extensions and improvements to IBM's system for automatic transcription of broadcast news. The speech recognizer uses a total of 160 hours of acoustic training data, 80 hours more than for the system described in [6]. In addition to improvements obtained in 1997 we made a number of changes and algorithmic enhancements. Among these were changing the acoustic vocabulary, reducing the number of phonemes, insertion of short pauses, mixture models consisting of non-Gaussian components, pronunciation networks, factor analysis (FACILT) and Bayesian Information Criteria (BIC) applied to choosing the number of components in a Gaussian mixture model. The models were combined in a single system using NIST's script voting machine known as rover.

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Publication

ICASSP 1999

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