用语调单元优化印地语语音合成，提升自然度与准确性。
Exploring an Inter-Pausal Unit (IPU) based Approach for Indic End-to-End TTS Systems
- 以语调单元替代句子作为合成单元，适配印地语长句特点。
- 相比传统方法，错误率降低，且生成语音更富韵律感。
- 适合开发高自然度对话式语音系统，尤其适用于资源有限场景。
印度语言的句子通常比英语更长，且以语义完整短语为单位组合成句。长句导致文本到语音模型训练困难，合成语音韵律不佳。本文探索在端到端框架中采用语调单元（IPU）的方法，聚焦对话风格语音合成。研究对比了自回归的Tacotron2与非自回归的FastSpeech2架构，在印地语、泰米尔语和泰卢固语三种语言上进行实验。基于IPU的Tacotron2在合成音频中显著降低了插入与删除错误，为减少错误提供了新路径。该方法计算开销更小，生成语音韵律更丰富，优于传统的句子级系统。
原文摘要 · Abstract (English)
Sentences in Indian languages are generally longer than those in English. Indian languages are also considered to be phrase-based, wherein semantically complete phrases are concatenated to make up sentences. Long utterances lead to poor training of text-to-speech models and result in poor prosody during synthesis. In this work, we explore an inter-pausal unit (IPU) based approach in the end-to-end (E2E) framework, focusing on synthesising conversational-style text. We consider both autoregressive Tacotron2 and non-autoregressive FastSpeech2 architectures in our study and perform experiments with three Indian languages, namely, Hindi, Tamil and Telugu. With the IPU-based Tacotron2 approach, we see a reduction in insertion and deletion errors in the synthesised audio, providing an alternative approach to the FastSpeech(2) network in terms of error reduction. The IPU-based approach requires less computational resources and produces prosodically richer synthesis compared to conventional sentence-based systems.
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