# Towards Developing State-of-the-Art TTS Synthesisers for 13 Indian Languages with Signal Processing aided Alignments

arXiv: https://arxiv.org/abs/2210.17153 · HTML: https://mlsanity.com/paper/2210.17153/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2022.

## Built on by (most cited first, 4 of 4)

- [ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams](https://mlsanity.com/paper/2410.17901/) (2024)
- [Phir Hera Fairy: An English Fairytaler is a Strong Faker of Fluent Speech in Low-Resource Indian Languages](https://mlsanity.com/paper/2505.20693/) (2025)
- [Everyday Speech in the Indian Subcontinent](https://mlsanity.com/paper/2410.10508/) (2024)
- [Kinship in Speech: Leveraging Linguistic Relatedness for Zero-Shot TTS in Indian Languages](https://mlsanity.com/paper/2506.03884/) (2025)

## Abstract (arXiv)

End-to-end (E2E) systems synthesise high-quality speech, but this typically requires a large amount of data. As E2E synthesis progressed from Tacotron to FastSpeech2, it became evident that features representing prosody, particularly sub-word durations, are important for error-free synthesis. Variants of FastSpeech use a teacher model or forced alignments for training. This paper uses signal processing cues in tandem with forced alignment to produce accurate phone boundaries for the training data. As a result of better duration modelling, good-quality synthesisers are developed. Evaluations indicate that systems developed using the proposed signal processing-aided approach are better than systems developed using other alignment approaches, especially in low-resource scenarios. Our systems also outperform the existing best TTS systems available for 13 Indian languages.