An impressionist is the one who tries to mimic other people’s voices and their style of speech. Humans have mastered such a task throughout the years. In this work, we introduce a deep learning-based approach to do voice conversion with speech style transfer across different speakers. In our work, we use a combination of Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN) as the main components of our proposed model followed by a WaveNet-based vocoder. We use three objective metrics to evaluate our model using the ASVspoof 2019 for measuring the difficulty of differentiating between human and synthesized samples, content ver-ification for transcription accuracy, and speaker encoding for identity verification. Our results show the efficacy of our proposed model in producing a high quality synthesized speech on Flickr8k audio corpus.
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Voice Conversion Using Speech-to-Speech Neuro-Style Transfer
Semantic Scholar · Computer Science · 2020
Abstract
An impressionist is the one who tries to mimic other people’s voices and their style of speech. Humans have mastered such a task throughout the years. In this work, we introduce a deep learning-based approach to do voice conversion with speech style transfer across different speakers. In our work, we use a combination of Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN) as the main components of our proposed model followed by a WaveNet-based vocoder. We use three objective metrics to evaluate our model using the ASVspoof 2019 for measuring the difficulty of differentiating between human and synthesized samples, content ver-ification for transcription accuracy, and speaker encoding for identity verification. Our results show the efficacy of our proposed model in producing a high quality synthesized speech on Flickr8k audio corpus.