NDLI: Noise robust speech parameterization using multiresolution feature extraction

Content Provider	IEEE Xplore Digital Library
Author	Hariharan, R. Kiss, I. Viikki, I.
Copyright Year	1993
Abstract	In this paper, we present a multiresolution-based feature extraction technique for speech recognition in adverse conditions. The proposed front-end algorithm uses mel cepstrum-based feature computation of subbands in order not to spread noise distortions over the entire feature space. Conventional full-band features are also augmented to the final feature vector which is fed to the recognition unit. Other novel features of the proposed front-end algorithm include emphasis of long-term spectral information combined with cepstral domain feature vector normalization and the use of the PCA transform, instead of DCT, to provide the final cepstral parameters. The proposed algorithm was experimentally evaluated in a connected digit recognition task under various noise conditions. The results obtained show that the new feature extraction algorithm improves word recognition accuracy by 41 % when compared to the performance of mel cepstrum front-end. A substantial increase in recognition accuracy was observed in all tested noise environments at all different SNRs. The good performance of the multiresolution front-end is not only due to the higher feature vector dimension, but the proposed algorithm clearly outperformed the mel cepstral front-end when the same number of HMM parameters were used in both systems. We also propose methods to reduce the computational complexity of the multiresolution front-end-based speech recognition system. Experimental results indicate the viability of the proposed techniques.
Starting Page	856
Ending Page	865
Page Count	10
File Size	140417
File Format	PDF
ISSN	10636676
Volume Number	9
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Speech enhancement Cepstral analysis Feature extraction Speech recognition Working environment noise Principal component analysis Discrete cosine transforms Cepstrum Testing
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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