Sparse imputation for noise robust speech recognition using soft masks

Authors:
J. F. Gemmeke;B. Cranen
Affiliations:
Dept. of Linguistics, Radboud University, P.O. Box 9103, NL-6500 HD, Nijmegen, The Netherlands;Dept. of Linguistics, Radboud University, P.O. Box 9103, NL-6500 HD, Nijmegen, The Netherlands
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

Citing 0
Cited 2

Missing data imputation using compressive sensing techniques for connected digit recognition

DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Sparse imputation for large vocabulary noise robust ASR

Computer Speech and Language

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Abstract

In previous work we introduced a new missing data imputation method for ASR, dubbed sparse imputation. We showed that the method is capable of maintaining good recognition accuracies even at very low SNRs provided the number of mask estimation errors is sufficiently low. Especially at low SNRs, however, mask estimation is difficult and errors are unavoidable. In this paper, we try to reduce the impact of mask estimation errors by making soft decisions, i.e., estimating the probability that a feature is reliable. Using an isolated digit recognition task (using the AURORA-2 database), we demonstrate that using soft masks in our sparse imputation approach yields a substantial increase in recognition accuracy, most notably at low SNRs.