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The Aurora Experimental Framework for the Performance Evaluation of Speech Recognition Systems under Noisy Conditions (2000)
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| Venue: | in ISCA ITRW ASR2000 |
| Citations: | 533 - 6 self |
BibTeX
@INPROCEEDINGS{Pearce00theaurora,
author = {David Pearce and Hans-günter Hirsch and Ericsson Eurolab Deutschland Gmbh},
title = {The Aurora Experimental Framework for the Performance Evaluation of Speech Recognition Systems under Noisy Conditions},
booktitle = {in ISCA ITRW ASR2000},
year = {2000},
pages = {29--32}
}
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Abstract
This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used to measure frontend feature extraction algorithms, using a defined HMM recognition back-end, or complete recognition systems. The source speech for this database is the TIdigits, consisting of connected digits task spoken by American English talkers (downsampled to 8kHz). A selection of 8 different real-world noises have been added to the speech over a range of signal to noise ratios with controlled filtering of the speech and noise. The framework was prepared as a contribution to the ETSI STQ-AURORA DSR Working Group[1]. Aurora is developing standards for Distributed Speech Recognition (DSR) where the speech analysis is done in the telecommunication terminal and the recognition at a central location in the telecom network. The framework is currently being used to evaluate alternative proposals for front-end feature extraction. The database has been made publicly available through ELRA so that other speech researchers to evaluate and compare the performance of noise robust algorithms. Recognition results will be presented for the first standard DSR feature extraction scheme based on a cepstral analysis. 1.
Keyphrases
noisy condition aurora experimental framework performance evaluation speech recognition system american english talker source speech speech analysis cepstral analysis central location recognition result telecommunication terminal front-end feature extraction alternative proposal complete recognition system different real-world noise defined hmm recognition back-end speech researcher controlled filtering speech recognition algorithm frontend feature extraction algorithm etsi stq-aurora dsr working group distributed speech recognition noise robust algorithm telecom network
