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Content Provider | IEEE Xplore Digital Library |
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Author | Hao-Teng Fan Jeih-weih Hung |
Copyright Year | 2012 |
Description | Author affiliation: Dept of Electrical Engineering, National Chi Nan University, Nantou, Taiwan (Hao-Teng Fan; Jeih-weih Hung) |
Abstract | In this paper, we propose to operate the sub-band division via discrete wavelet transform (DWT) before the process of gain normalization (GN) in producing speech features. In the presented approach, we apply the DWT to decompose the temporal-domain cepstral feature sequence, and then perform the gain normalization on each sub-band feature stream. Finally, the new feature stream is obtained by the inverse DWT of all sub-band streams. Compared with the gain normalization process directly performed on the original full-band stream, the presented approach can deal with the sub-band distortions individually and is expected to be more noise-robust. In the Aurora-2 database and task, this new sub-band GN outperforms the baseline process and the original full-band GN by 65.51% and 18.20% in relative word error reduction. |
Starting Page | 1409 |
Ending Page | 1412 |
File Size | 407831 |
Page Count | 4 |
File Format | |
ISBN | 9781467300254 |
e-ISBN | 9781467300247 |
DOI | 10.1109/FSKD.2012.6234339 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-05-29 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Training Accuracy Frequency modulation robust speech feature speech recognition Speech recognition Speech discrete wavelet transform Discrete wavelet transforms |
Content Type | Text |
Resource Type | Article |
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