Details

Autor(en) / Beteiligte

• Lin, Chin-Teng
• Liu, Der-Jenq
• Wu, Rui-Cheng
• Wu, Gin-Der
• Pal, Nikhil R.
• Sugeno, Michio

Titel

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

Ist Teil von

• Advances in Soft Computing — AFSS 2002, 2002, p.301-309

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2002

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 TFbased robust algorithm by 12%.