A Unified Framework for Collecting Text-to-Speech Synthesis Datasets for 22 Indian Languages

TL;DR

This paper reviews 15 years of data collection efforts for Indian languages, emphasizing the importance of data quality and standardized protocols for developing effective TTS systems across diverse languages.

Contribution

It presents a unified framework and insights for collecting high-quality TTS datasets for Indian languages, facilitating consistent development across multiple languages.

Findings

High-quality data enables effective TTS with smaller datasets.

Standardized data collection protocols improve TTS system development.

Collected datasets have supported various TTS frameworks and prosodically rich synthesis.

Abstract

The performance of a text-to-speech (TTS) synthesis model depends on various factors, of which the quality of the training data is of utmost importance. Millions of data are collected around the globe for various languages, but resources for Indian languages are few. Although there are many efforts involved in data collection, a common set of protocols for data collection becomes necessary for building TTS systems in Indian languages primarily because of the need for a uniform development of TTS systems across languages. In this paper, we present our learnings on data collection efforts' for Indic languages over 15 years. These databases have been used in unit selection synthesis, hidden Markov model based, and end-to-end frameworks, and for generating prosodically rich TTS systems. The most significant feature of the data collected is that data purity enables building high-quality TTS systems with a comparatively small dataset compared to that of European/Chinese languages.