標題: Noisy speech segmentation/enhancement with multiband analysis and neural fuzzy networks
作者: Lin, CT
Wu, RC
Wu, GD
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: mel-scale frequency;multiband;spectrum analysis;self-learning ability;neural fuzzy network
公開日期: 1-十一月-2002
摘要: This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.
URI: http://dx.doi.org/10.1142/S0218001402002076
http://hdl.handle.net/11536/28438
ISSN: 0218-0014
DOI: 10.1142/S0218001402002076
期刊: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume: 16
Issue: 7
起始頁: 927
結束頁: 955
顯示於類別:期刊論文


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