Effectiveness of Word String Language Models on Noisy Broadcast News Speech Recognition

Kazuyuki TAKAGI; Rei OGURO; Kazuhiko OZEKI

Effectiveness of Word String Language Models on Noisy Broadcast News Speech Recognition

Kazuyuki TAKAGI, Rei OGURO, Kazuhiko OZEKI

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Experiments were conducted to examine an approach from language modeling side to improving noisy speech recognition performance. By adopting appropriate word strings as new units of processing, speech recognition performance was improved by acoustic effects as well as by test-set perplexity reduction. Three kinds of word string language models were evaluated, whose additional lexical entries were selected based on combinations of part of speech information, word length, occurrence frequency, and log likelihood ratio of the hypotheses about the bigram frequency. All of the three word string models reduced errors in broadcast news speech recognition, and also lowered test-set perplexity. The word string model based on log likelihood ratio exhibited the best improvement for noisy speech recognition, by which deletion errors were reduced by 26%, substitution errors by 9.3%, and insertion errors by 13%, in the experiments using the speaker-dependent, noise-adapted triphone. Effectiveness of word string models on error reduction was more prominent for noisy speech than for studio-clean speech.

Publication: IEICE TRANSACTIONS on Information Vol.E85-D No.7 pp.1130-1137

Publication Date: 2002/07/01

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Type of Manuscript: PAPER

Category: Speech and Hearing

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Kazuyuki TAKAGI, Rei OGURO, Kazuhiko OZEKI, "Effectiveness of Word String Language Models on Noisy Broadcast News Speech Recognition" in IEICE TRANSACTIONS on Information, vol. E85-D, no. 7, pp. 1130-1137, July 2002, doi: .
Abstract: Experiments were conducted to examine an approach from language modeling side to improving noisy speech recognition performance. By adopting appropriate word strings as new units of processing, speech recognition performance was improved by acoustic effects as well as by test-set perplexity reduction. Three kinds of word string language models were evaluated, whose additional lexical entries were selected based on combinations of part of speech information, word length, occurrence frequency, and log likelihood ratio of the hypotheses about the bigram frequency. All of the three word string models reduced errors in broadcast news speech recognition, and also lowered test-set perplexity. The word string model based on log likelihood ratio exhibited the best improvement for noisy speech recognition, by which deletion errors were reduced by 26%, substitution errors by 9.3%, and insertion errors by 13%, in the experiments using the speaker-dependent, noise-adapted triphone. Effectiveness of word string models on error reduction was more prominent for noisy speech than for studio-clean speech.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_7_1130/_p

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@ARTICLE{e85-d_7_1130,
author={Kazuyuki TAKAGI, Rei OGURO, Kazuhiko OZEKI, },
journal={IEICE TRANSACTIONS on Information},
title={Effectiveness of Word String Language Models on Noisy Broadcast News Speech Recognition},
year={2002},
volume={E85-D},
number={7},
pages={1130-1137},
abstract={Experiments were conducted to examine an approach from language modeling side to improving noisy speech recognition performance. By adopting appropriate word strings as new units of processing, speech recognition performance was improved by acoustic effects as well as by test-set perplexity reduction. Three kinds of word string language models were evaluated, whose additional lexical entries were selected based on combinations of part of speech information, word length, occurrence frequency, and log likelihood ratio of the hypotheses about the bigram frequency. All of the three word string models reduced errors in broadcast news speech recognition, and also lowered test-set perplexity. The word string model based on log likelihood ratio exhibited the best improvement for noisy speech recognition, by which deletion errors were reduced by 26%, substitution errors by 9.3%, and insertion errors by 13%, in the experiments using the speaker-dependent, noise-adapted triphone. Effectiveness of word string models on error reduction was more prominent for noisy speech than for studio-clean speech.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - Effectiveness of Word String Language Models on Noisy Broadcast News Speech Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1130
EP - 1137
AU - Kazuyuki TAKAGI
AU - Rei OGURO
AU - Kazuhiko OZEKI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2002
AB - Experiments were conducted to examine an approach from language modeling side to improving noisy speech recognition performance. By adopting appropriate word strings as new units of processing, speech recognition performance was improved by acoustic effects as well as by test-set perplexity reduction. Three kinds of word string language models were evaluated, whose additional lexical entries were selected based on combinations of part of speech information, word length, occurrence frequency, and log likelihood ratio of the hypotheses about the bigram frequency. All of the three word string models reduced errors in broadcast news speech recognition, and also lowered test-set perplexity. The word string model based on log likelihood ratio exhibited the best improvement for noisy speech recognition, by which deletion errors were reduced by 26%, substitution errors by 9.3%, and insertion errors by 13%, in the experiments using the speaker-dependent, noise-adapted triphone. Effectiveness of word string models on error reduction was more prominent for noisy speech than for studio-clean speech.
ER -