Lightweight, Multi-Speaker, Multi-Lingual Indic Text-to-Speech.

Abhayjeet Singh, Amala Nagireddi,Deekshitha G,Jesuraja Bandekar, Roopa R.,Sandhya Badiger,Sathvik Udupa,Prasanta Kumar Ghosh,Hema A. Murthy,Heiga Zen,Pranaw Kumar, Kamal Kant, Amol Bole, Bira Chandra Singh,Keiichi Tokuda,Mark Hasegawa-Johnson, Philipp Olbrich

ICASSP（2023）

引用 0|浏览16

暂无评分

摘要

The Lightweight, Multi-speaker, Multi-lingual Indic Text-to-Speech (LIMMITS'23) challenge is organized as part of the ICASSP 2023 Signal Processing Grand Challenge. LIMMITS'23 aims at the development of a lightweight, multi-speaker, multi-lingual Text to Speech (TTS) model using datasets in Marathi, Hindi, and Telugu, with at least 40 hours of data released for each of the male and female voice artists in each language. The challenge encourages the advancement of TTS in Indian Languages as well as the development of techniques involved in TTS data selection and model compression. The 3 tracks of LIMMITS'23 have provided an opportunity for various researchers and practitioners around the world to explore the state-of-the-art techniques in TTS research.

查看译文

关键词

end-to-end model,data-constrained multi-speaker,model compression,multi-lingual TTS,speech synthesis,Text-to-Speech (TTS)

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要