Time-frequency correlation based missing-feature reconstruction for robust speech recognition in background noise conditions

Authors:
Wooil Kim;John H. L. Hansen
Affiliations:
Center for Robust Speech Systems, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, Texas;Center for Robust Speech Systems, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, Texas
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

Citing 4
Cited 0

Data-driven environmental compensation for speech recognition: a unified approach

Speech Communication
Robust automatic speech recognition with missing and unreliable acoustic data

Speech Communication
Feature compensation in the cepstral domain employing model combination

Speech Communication
Time-frequency correlation-based missing-feature reconstruction for robust speech recognition in band-restricted conditions

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This study proposes a novel missing-feature reconstruction method to improve speech recognition in background noise environments. In order to improve the existing missing-feature reconstruction method which utilizes only frequency correlation, a temporal spectral feature analysis is employed to leverage temporal correlation across neighboring frames. The final estimates for missing-feature reconstruction are obtained by a selective combination of the frequency correlation based method and the proposed temporal correlation based method. Performance of the proposed method is evaluated using the Aurora 2.0 framework with car noise and speech babble conditions. Experimental results demonstrate that the proposed method is more effective at increasing speech recognition performance in adverse conditions. By employing the proposed temporal-frequency based reconstruction method with SNR-based mask estimation, +21.31% and +20.73% average relative improvements in WER are obtained for car and speech babble conditions, compared to the original frequency correlation based method.