LeanSpeech: The Microsoft Lightweight Speech Synthesis System for Limmits Challenge 2023
Chen Zhang, Shubham Bansal, Aakash Lakhera, Jinzhu Li, Gang Wang, Sandeepkumar Satpal, Sheng Zhao, Lei He
Abstract
This paper describes the Microsoft Text-to-Speech (TTS) system: LeanSpeech for LIMMITS (Lightweight, Multi-speaker, Multi-lingual Indic TTS) Challenge 2023<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, 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.
BibTeX
@inproceedings{icassp2023_leanspeechthemic,
title = {LeanSpeech: The Microsoft Lightweight Speech Synthesis System for Limmits Challenge 2023},
author = {Chen Zhang and Shubham Bansal and Aakash Lakhera and Jinzhu Li and Gang Wang and Sandeepkumar Satpal and Sheng Zhao and Lei He},
booktitle = {ICASSP 2023},
year = {2023}
}