Exploring An Inter-pausal Unit (IPU) Based Approach For Indic End-to-end TTS Systems

Abstract

Sentences in Indian languages are generally longer than those in English. Indian languages are also considered to be phrase-based, wherein semantically complete phrases are concatenated to make up sentences. Long utterances lead to poor training of text-to-speech models and result in poor prosody during synthesis. In this work, we explore an inter-pausal unit (IPU) based approach in the end-to-end (E2E) framework, focusing on synthesising conversational-style text. We consider both autoregressive Tacotron2 and non-autoregressive FastSpeech2 architectures in our study and perform experiments with three Indian languages, namely, Hindi, Tamil and Telugu. With the IPU-based Tacotron2 approach, we see a reduction in insertion and deletion errors in the synthesised audio, providing an alternative approach to the FastSpeech(2) network in terms of error reduction. The IPU-based approach requires less computational resources and produces prosodically richer synthesis compared to conventional se

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyprakash2024exploring

Related papers

• Towards Developing State-of-the-art TTS Synthesisers For 13 Indian Languages With Signal Processing Aided Alignments (2022)0.00
• Generic Indic Text-to-speech Synthesisers With Rapid Adaptation In An End-to-end Framework (2020)8.82
• Towards Building Text-to-speech Systems For The Next Billion Users (2022)0.00
• A Unified Framework For Collecting Text-to-speech Synthesis Datasets For 22 Indian Languages (2024)0.00
• Rapid Speaker Adaptation In Low Resource Text To Speech Systems Using Synthetic Data And Transfer Learning (2023)0.00
• Praxy Voice: Voice-prompt Recovery + BUPS For Commercial-class Indic TTS From A Frozen Non-indic Base At Zero Commercial-training-data Cost (2026)0.00
• Paratts: Learning Linguistic And Prosodic Cross-sentence Information In Paragraph-based TTS (2022)8.82
• ELAICHI: Enhancing Low-resource TTS By Addressing Infrequent And Low-frequency Character Bigrams (2024)0.00