A neural-wavelet architecture for voice conversion

In this letter we propose a new architecture for voice conversion that is based on a joint neural-wavelet approach. We also examine the characteristics of many wavelet families and determine the one that best matches the requirements of the proposed system. The conclusions presented in theory are confirmed in practice with utterances extracted from TIMIT speech corpus.

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