An iterative longest matching segment approach to speech enhancement with additive noise and channel distortion |
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| Affiliation: | 1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, 18 Malone Road, BT9 6RT Belfast, United Kingdom;2. Department of Computer Science and Creative Technologies, University of the West of England, Coldharbour Lane, BS16 1QY Bristol, United Kingdom;3. Merchant Venturers School of Engineering, University of Bristol, 75 Woodland Road, BS8 1UB Bristol, United Kingdom;4. Centre for Public Health, Queen’s University Belfast, Institute of Clinical Sciences, Block B, BT12 6BA Belfast, United Kingdom;1. Signal and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, sn, Ed. de Telecomunicación, Pabellón B, Despacho 111, E35017 Las Palmas de Gran Canaria, Spain;2. School of Computing, University of Dundee, Scotland, United Kingdom;3. The Institute of Electronics, Communications and Information Technology, Queen''s University Belfast, Northern Ireland Science Park, Queen''s Road, Queen''s Island, BT3 9DT Belfast, United Kingdom |
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| Abstract: | This paper presents a new approach to speech enhancement from single-channel measurements involving both noise and channel distortion (i.e., convolutional noise), and demonstrates its applications for robust speech recognition and for improving noisy speech quality. The approach is based on finding longest matching segments (LMS) from a corpus of clean, wideband speech. The approach adds three novel developments to our previous LMS research. First, we address the problem of channel distortion as well as additive noise. Second, we present an improved method for modeling noise for speech estimation. Third, we present an iterative algorithm which updates the noise and channel estimates of the corpus data model. In experiments using speech recognition as a test with the Aurora 4 database, the use of our enhancement approach as a preprocessor for feature extraction significantly improved the performance of a baseline recognition system. In another comparison against conventional enhancement algorithms, both the PESQ and the segmental SNR ratings of the LMS algorithm were superior to the other methods for noisy speech enhancement. |
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| Keywords: | Corpus-based speech modeling Longest matching segment Noisy speech Channel distortion Speech enhancement Speech recognition |
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