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arXiv · 2308.14909

Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech

Hyungchan Yoon·Changhwan Kim·Eunwoo Song·Hyun-Wook Yoon·Hong-Goo Kang
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Abstract

For personalized speech generation, a neural text-to-speech (TTS) model must be successfully implemented with limited data from a target speaker. To this end, the baseline TTS model needs to be amply generalized to out-of-domain data (i.e., target speaker's speech). However, approaches to address this out-of-domain generalization problem in TTS have yet to be thoroughly studied. In this work, we propose an effective pruning method for a transformer known as sparse attention, to improve the TTS model's generalization abilities. In particular, we prune off redundant connections from self-attention layers whose attention weights are below the threshold. To flexibly determine the pruning strength for searching optimal degree of generalization, we also propose a new differentiable pruning method that allows the model to automatically learn the thresholds. Evaluations on zero-shot multi-speaker TTS verify the effectiveness of our method in terms of voice quality and speaker similarity.

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Hyungchan Yoon, Changhwan Kim, Eunwoo Song, Hyun-Wook Yoon, Hong-Goo Kang. 2023-08-28. Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech. https://doi.org/10.21437/interspeech.2023-1301

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