Robust Speaker Recognition Using Denoised Vocal Source and Vocal Tract Features
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摘要To alleviate the problem of severe degradation of speaker recognition performance under noisy environments because of inadequate and inaccurate speaker-discriminative information, a method of robust feature estimation that can capture both vocal source-and vocal tract-related characteristics from noisy speech utterances is proposed. Spectral subtraction, a simple yet useful speech enhancement technique, is employed to remove the noise-specific components prior to the feature extraction process. It has been shown through analytical derivation, as well as by simulation results, that the proposed feature estimation method leads to robust recognition performance, especially at low signal-to-noise ratios. In the context of Gaussian mixture model-based speaker recognition with the presence of additive white Gaussian noise, the new approach produces consistent reduction of both identification error rate and equal error rate at signal-to-noise ratios ranging from 0 to 15 dB.
著者Wang N, Ching PC, Zheng NH, Lee T
期刊名稱IEEE Transactions on Audio, Speech and Language Processing
出版年份2011
月份1
日期1
卷號19
期次1
出版社IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
頁次196 - 205
國際標準期刊號1558-7916
電子國際標準期刊號1558-7924
語言英式英語
關鍵詞Robust parameter estimation; source-tract features; speaker recognition; spectral subtraction
Web of Science 學科類別Acoustics; ACOUSTICS; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2020-19-10 於 00:39