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

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innate

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滔滔不绝的人

障碍物

Defence and Security: New Issues and Impactsecognition 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.

archaeology

https://doi.org/10.1007/978-3-319-52836-6quency 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.

debase

Incumbent

CURT

Emerging Technologies for EducationLike ASR, emotion recognition can benefit from the merits of wavelet analysis. Similar methodologies may be followed based on WT similar to that used in speech recognition. Mainly, it is realized in literatures that WP parameters are responsive to emotions. Also, many results prove that wavelet-based features improve emotion recognition.

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