Recognizing reverberant speech with RASTA-PLP

Abstract

The performance of the PLP (perceptual linear predictive), log-RASTA-PLP, and J-RASTA-PLP front ends for recognition of highly reverberant speech is measured and compared with the performance of humans and the performance of an experimental RASTA-like front end on reverberant speech, and with the performance of a PLP-based recognizer trained on reverberant speech. While humans are able to reliably recognize the reverberant test set, achieving a 6.1% word error rate, the best RASTA-PLP-based recognizer has a word error rate of 68.7% on the same test set, and the PLP-based recognizer trained on reverberant speech has a 50.3% word error rate. Our experimental variant on RASTA processing provides a statistically significant improvement in performance on the reverberant speech, with a best word error rate of 64.1%.