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

IF 2.9 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Abhayjeet Singh;Amala Nagireddi;Anjali Jayakumar;Deekshitha G;Jesuraja Bandekar;Roopa R;Sandhya Badiger;Sathvik Udupa;Saurabh Kumar;Prasanta Kumar Ghosh;Hema A Murthy;Heiga Zen;Pranaw Kumar;Kamal Kant;Amol Bole;Bira Chandra Singh;Keiichi Tokuda;Mark Hasegawa-Johnson;Philipp Olbrich
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引用次数: 0

Abstract

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.
轻量级、多扬声器、多语言 Indic 文本到语音技术
轻量级、多扬声器、多语言印地语文本到语音(LIMMITS'23)挑战赛是 ICASSP 2023 信号处理大挑战赛的一部分。LIMMITS'23 的目标是使用马拉地语、印地语和泰卢固语数据集开发轻量级、多扬声器、多语言文本到语音 (TTS) 模型,每种语言的男女语音艺术家都要发布至少 40 小时的数据。该挑战赛鼓励印度语言中的语音合成技术的进步,以及语音合成技术数据选择和模型压缩技术的发展。LIMMITS'23 的 3 个分会场为世界各地的研究人员和从业人员提供了探索 TTS 研究领域最先进技术的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.30
自引率
0.00%
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0
审稿时长
22 weeks
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