Titlebook: Application of Wavelets in Speech Processing; Mohamed Hesham Farouk Book 2018Latest edition The Author(s) 2018 Multiresolution Analysis.Sh

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Speech Recognition,ecognition process may perform better. Alternatively, wavelet-based features can be added to other successful features to improve recognition performance. Third, wavelets can serve as an activation function in neural-networks employed for speech recognition. Hybrid methodology may comprise a mix of one or more approaches.

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Speaker Identification,quency regions while the Mel scale gets coarser in the higher-frequency bands. The speaker’s individual information, which is nonuniformly distributed in the high-frequency bands, is equally important for speaker recognition. Accordingly, wavelet-based features are more appropriate.

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Spectral Analysis of Speech Signal and Pitch Estimation,pectral estimation for speech signal than other methods. Wavelet-based pitch estimation assumes that the glottis closures are correlated with the maxima in the adjacent scales of the WT. This approach ensures more accurate estimation of pitch period.

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Amjad Fayoumi,Juliana Sutanto,Andreas Mautheh can be improved through the use of wavelet tools. Researchers and practitioners in speech technology will find valuable information in this monograph on the use of wavelets to strengthen both development and research in different applications of speech processing.

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Ting-Ting Wu,Rosella Gennari,Yiwei Cao quantization error. Experimental results show that WT-based coders deliver superior quality to some audio standards when operating at the same bit rate and they deliver comparable quality to other codecs at lower bit rates. As a result, speech coding with WT can provide an efficient and flexible scheme for audio compression.