Lightweight, Multi-speaker, Multi-lingual Indic Text-To-Speech

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalIEEE Open Journal of Signal Processing
DOIs
StateAccepted/In press - 2024

Keywords

  • Computational modeling
  • Data models
  • Distortion
  • Histograms
  • Solid modeling
  • Text-to-Speech (TTS)
  • Training
  • Vocabulary
  • data-constrained multi-speaker
  • end-to-end model
  • model compression
  • multi-lingual TTS
  • speech synthesis

ASJC Scopus subject areas

  • Signal Processing

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