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Feature extraction using non-linear transformation for robust speech recognition on the Aurora database

Sangita Sharma; Daniel P. W. Ellis; Sachin Kajarekar; Pratibha Jain; Hynek Hermansky

Title:
Feature extraction using non-linear transformation for robust speech recognition on the Aurora database
Author(s):
Sharma, Sangita
Ellis, Daniel P. W.
Kajarekar, Sachin
Jain, Pratibha
Hermansky, Hynek
Date:
Type:
Articles
Department:
Electrical Engineering
Permanent URL:
Book/Journal Title:
2000 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings, 5-9 June, 2000, Hilton Hotel and Convention Center, Istanbul, Turkey
Publisher:
IEEE
Publisher Location:
Piscataway, N.J.
Abstract:
We evaluate the performance of several feature sets on the Aurora task as defined by ETSI. We show that after a non-linear transformation, a number of features can be effectively used in a HMM-based recognition system. The non-linear transformation is computed using a neural network which is discriminatively trained on the phonetically labeled (forcibly aligned) training data. A combination of the non-linearly transformed PLP (perceptive linear predictive coefficients), MSG (modulation filtered spectrogram) and TRAP (temporal pattern) features yields a 63% improvement in error rate as compared to baseline me frequency cepstral coefficients features. The use of the non-linearly transformed RASTA-like features, with system parameters scaled down to take into account the ETSI imposed memory and latency constraints, still yields a 40% improvement in error rate.
Subject(s):
Electrical engineering
Artificial intelligence
Publisher DOI:
http://dx.doi.org/10.1109/ICASSP.2000.859160
Item views:
42
Metadata:
text | xml

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