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      Voice Conversion for Whispered Speech Synthesis

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          Abstract

          We present an approach to synthesize whisper by applying a handcrafted signal processing recipe and Voice Conversion (VC) techniques to convert normally phonated speech to whispered speech. We investigate using Gaussian Mixture Models (GMM) and Deep Neural Networks (DNN) to model the mapping between acoustic features of normal speech and those of whispered speech. We evaluate naturalness and speaker similarity of the converted whisper on an internal corpus and on the publicly available wTIMIT corpus. We show that applying VC techniques is significantly better than using rule-based signal processing methods and it achieves results that are indistinguishable from copy-synthesis of natural whisper recordings. We investigate the ability of the DNN model to generalize on unseen speakers, when trained with data from multiple speakers. We show that excluding the target speaker from the training set has little or no impact on the perceived naturalness and speaker similarity of the converted whisper. The proposed DNN method is used in the newly released Whisper Mode of Amazon Alexa.

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          Author and article information

          Journal
          11 December 2019
          Article
          1912.05289
          3e7b1d93-b837-4b6b-87b1-aea89e031c11

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Submitted to IEEE Signal Processing Letters
          cs.SD cs.CL eess.AS

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