Noisy Speech Segmentation/Enhancement with Multiband Analysis and Neural Fuzzy Networks

Authors:
Chin-Teng Lin;Der-Jenq Liu;Rui-Cheng Wu;Gin-Der Wu
Affiliations:
-;-;-;-
Venue:
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Year:
2002

Citing 5
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Abstract

Background noise added to speech can decrease the performance of speech segmentation and enhancement. To solve this problem, new methods have been developed in this thesis. First, a new speech segmentation method (ATF-based SONFIN algorithm) is proposed in fixed noise-level environment. This method contains the multiband analysis and a neural fuzzy network, and it achieves higher recognition rate than the TF-based robust algorithm by 5%. In addition, a new speech segmentation method called RTF-based RSONFIN algorithm is proposed for variable noise-level environment. The RTF-based RSONFIN algorithm contains a recurrent neural fuzzy network. This method contains the multiband analysis and achieve higher recognition rate than the TF-based robust algorithm by 12%.