An uncertainty decoding approach to noise- and reverberation-robust speech recognition
- 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.
Author: | Roland Maas, Akshaya Thippur, Armin Sehr, Walter Kellermann |
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DOI: | https://doi.org/10.1109/ICASSP.2013.6639098 |
Parent Title (English): | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 26-31 May 2013, Vancouver, BC, Canada : Proceedings |
Publisher: | IEEE |
Document Type: | conference proceeding (article) |
Language: | English |
Year of first Publication: | 2013 |
Release Date: | 2022/11/25 |
Tag: | Adaptation models; Hidden Markov models; Noise; Optimization; Reverberation; Speech; Vectors |
First Page: | 7388 |
Last Page: | 7392 |
Institutes: | Fakultät Elektro- und Informationstechnik |
Fakultät Elektro- und Informationstechnik / Labor Elektroakustik | |
Begutachtungsstatus: | peer-reviewed |
Publication: | Externe Publikationen |
research focus: | Digitalisierung |
Licence (German): | Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG |