An uncertainty decoding approach to noise- and reverberation-robust speech recognition

Maas R, Thippur A, Sehr A, Kellermann W (2013)


Publication Language: English

Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2013

Pages Range: 7388-7392

Article Number: 6639098

Event location: Vancouver, BC CA

ISBN: 978-1-4799-0356-6

DOI: 10.1109/ICASSP.2013.6639098

Abstract

The generic REMOS (REverberation MOdeling for robust Speech recognition) concept is extended in this contribution to cope with additional noise components. REMOS originally embeds an explicit reverberation model into a hiddenMarkov model (HMM) leading to a relaxed conditional independence assumption for the observed feature vectors. During recognition, a nonlinear optimization problem is to be solved in order to adapt the HMMs' output probability density functions to the current reverberation conditions. The extension for additional noise components necessitates a modified numerical solver for the nonlinear optimization problem. We propose an approximation scheme based on continuous piecewise linear regression. Connected-digit recognition experiments demonstrate the potential of REMOS in reverberant and noisy environments. They furthermore reveal that the benefit of an explicit reverberation model, overcoming the conditional independence assumption, increases with increasing signal-to-noise-ratios. © 2013 IEEE.

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APA:

Maas, R., Thippur, A., Sehr, A., & Kellermann, W. (2013). An uncertainty decoding approach to noise- and reverberation-robust speech recognition. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 7388-7392). Vancouver, BC, CA.

MLA:

Maas, Roland, et al. "An uncertainty decoding approach to noise- and reverberation-robust speech recognition." Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, BC 2013. 7388-7392.

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