Generic Indic Text-to-speech Synthesisers With Rapid Adaptation In An End-to-end Framework

Abstract

Building text-to-speech (TTS) synthesisers for Indian languages is a difficult task owing to a large number of active languages. Indian languages can be classified into a finite set of families, prominent among them, Indo-Aryan and Dravidian. The proposed work exploits this property to build a generic TTS system using multiple languages from the same family in an end-to-end framework. Generic systems are quite robust as they are capable of capturing a variety of phonotactics across languages. These systems are then adapted to a new language in the same family using small amounts of adaptation data. Experiments indicate that good quality TTS systems can be built using only 7 minutes of adaptation data. An average degradation mean opinion score of 3.98 is obtained for the adapted TTSes. Extensive analysis of systematic interactions between languages in the generic TTSes is carried out. x-vectors are included as speaker embedding to synthesise text in a particular speaker's voice. An in

Stats

• citations14
• S2 citations—
• github stars0
• HF likes0
• heat score8.82
• arxiv keyprakash2020generic

Related papers

• Towards Building Text-to-speech Systems For The Next Billion Users (2022)0.00
• Rapid Speaker Adaptation In Low Resource Text To Speech Systems Using Synthetic Data And Transfer Learning (2023)0.00
• A Unified Framework For Collecting Text-to-speech Synthesis Datasets For 22 Indian Languages (2024)0.00
• Towards Developing State-of-the-art TTS Synthesisers For 13 Indian Languages With Signal Processing Aided Alignments (2022)0.00
• Indicvoices-r: Unlocking A Massive Multilingual Multi-speaker Speech Corpus For Scaling Indian TTS (2024)2.26
• Empowering Global Voices: A Data-efficient, Phoneme-tone Adaptive Approach To High-fidelity Speech Synthesis (2025)0.00
• Exploring An Inter-pausal Unit (IPU) Based Approach For Indic End-to-end TTS Systems (2024)0.00
• Fast And Small Footprint Hybrid Hmm-hifigan Based System For Speech Synthesis In Indian Languages (2023)0.00