Multi-modal Adversarial Training for Zero-Shot Voice Cloning

• URL: http://arxiv.org/abs/2408.15916v1
• Date: Wed, 28 Aug 2024 16:30:41 GMT
• Title: Multi-modal Adversarial Training for Zero-Shot Voice Cloning
• Authors: John Janiczek, Dading Chong, Dongyang Dai, Arlo Faria, Chao Wang, Tao Wang, Yuzong Liu,
• Abstract summary: We propose a Transformer encoder-decoder architecture to conditionally discriminate between real and generated speech features. We introduce our novel adversarial training technique by applying it to a FastSpeech2 acoustic model and training on Libriheavy, a large multi-speaker dataset. Our model achieves improvements over the baseline in terms of speech quality and speaker similarity.
• Score: 9.823246184635103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A text-to-speech (TTS) model trained to reconstruct speech given text tends towards predictions that are close to the average characteristics of a dataset, failing to model the variations that make human speech sound natural. This problem is magnified for zero-shot voice cloning, a task that requires training data with high variance in speaking styles. We build off of recent works which have used Generative Advsarial Networks (GAN) by proposing a Transformer encoder-decoder architecture to conditionally discriminates between real and generated speech features. The discriminator is used in a training pipeline that improves both the acoustic and prosodic features of a TTS model. We introduce our novel adversarial training technique by applying it to a FastSpeech2 acoustic model and training on Libriheavy, a large multi-speaker dataset, for the task of zero-shot voice cloning. Our model achieves improvements over the baseline in terms of speech quality and speaker similarity. Audio examples from our system are available online.

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