Chen Zhang, Shubham Bansal, Aakash Lakhera, Jinzhu Li, G. Wang, Sandeepkumar Satpal, Sheng Zhao, Lei He
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引用次数: 0
Abstract
This paper describes the Microsoft Text-to-Speech (TTS) system: LeanSpeech for LIMMITS (Lightweight, Multi-speaker, Multi-lingual Indic TTS) Challenge 20231, which is part of ICASSP2023 to encourage the advance of TTS in Indian Languages. We propose a lightweight encoder-decoder acoustic model composed of 1-D convolution and LSTM blocks, which is trained with knowledge distillation from a multi-speaker multi-lingual teacher model, DelightfulTTS [1]. The speech corpus is reprocessed and used in both AM training and vocoder fine-tuning. In Track-2 of the challenge, our system achieves MOS 4.56 and SMOS 3.98, which indicates the efficiency of the proposed model and training strategy.
本文描述了微软文本到语音(TTS)系统:LeanSpeech for LIMMITS(轻量级,多扬声器,多语言印度TTS)挑战20231,这是ICASSP2023的一部分,旨在鼓励印度语言TTS的进步。我们提出了一个轻量级的编码器-解码器声学模型,该模型由一维卷积和LSTM块组成,该模型使用来自多说话多语言教师模型DelightfulTTS[1]的知识蒸馏进行训练。语音语料库被重新处理并用于AM训练和声码器微调。在Track-2中,我们的系统达到了SMOS 4.56和SMOS 3.98,表明了所提出的模型和训练策略的有效性。