用自动引导提升低资源语音适配效果，让小模型也能搞定陌生声音。
VoiceGuider: Enhancing Out-of-Domain Performance in Parameter-Efficient Speaker-Adaptive Text-to-Speech via Autoguidance
- 通过自动引导机制增强LoRA微调的语音适应能力
- 在极端域外语音上表现显著优于普通参数高效方法
- 适合资源受限场景下需跨说话人适配的语音合成应用
在参数高效的LoRA微调应用于说话人自适应文本到语音模型时，对域外说话人的适配性能可能低于全量微调模型。本文提出VoiceGuider，一种基于自动引导的参数高效语音合成系统，旨在缩小与全量微调模型的性能差距。我们系统探索了多种强化自动引导的策略，最终确定最优方案。实验表明，该方法在极端域外语音数据上表现出稳健的适应能力。演示页面提供可听样本。
原文摘要 · Abstract (English)
When applying parameter-efficient finetuning via LoRA onto speaker adaptive text-to-speech models, adaptation performance may decline compared to full-finetuned counterparts, especially for out-of-domain speakers. Here, we propose VoiceGuider, a parameter-efficient speaker adaptive text-to-speech system reinforced with autoguidance to enhance the speaker adaptation performance, reducing the gap against full-finetuned models. We carefully explore various ways of strengthening autoguidance, ultimately finding the optimal strategy. VoiceGuider as a result shows robust adaptation performance especially on extreme out-of-domain speech data. We provide audible samples in our demo page.
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