# Towards Building Text-To-Speech Systems for the Next Billion Users

arXiv: https://arxiv.org/abs/2211.09536 · HTML: https://mlsanity.com/paper/2211.09536/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://mlsanity.com/paper/1811.00002/) (2018)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search](https://mlsanity.com/paper/2005.11129/) (2020)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Diff-TTS: A Denoising Diffusion Model for Text-to-Speech](https://mlsanity.com/paper/2104.01409/) (2021)
- [FastPitch: Parallel Text-to-speech with Pitch Prediction](https://mlsanity.com/paper/2006.06873/) (2020)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [High Fidelity Speech Synthesis with Adversarial Networks](https://mlsanity.com/paper/1909.11646/) (2019)
- [Deep Voice 2: Multi-Speaker Neural Text-to-Speech](https://mlsanity.com/paper/1705.08947/) (2017)
- [Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://mlsanity.com/paper/1710.07654/) (2017)
- [Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech](https://mlsanity.com/paper/2005.05106/) (2020)
- [StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion](https://mlsanity.com/paper/2107.10394/) (2021)
- [One TTS Alignment To Rule Them All](https://mlsanity.com/paper/2108.10447/) (2021)
- [Towards Building ASR Systems for the Next Billion Users](https://mlsanity.com/paper/2111.03945/) (2021)
- [Revisiting Over-Smoothness in Text to Speech](https://mlsanity.com/paper/2202.13066/) (2022)
- [End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning](https://mlsanity.com/paper/1904.06508/) (2019)
- [Assessing Evaluation Metrics for Speech-to-Speech Translation](https://mlsanity.com/paper/2110.13877/) (2021)

## Built on by (most cited first, 27 of 27)

- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS](https://mlsanity.com/paper/2409.05356/) (2024)
- [Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings](https://mlsanity.com/paper/2407.14056/) (2024)
- [Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation](https://mlsanity.com/paper/2411.12719/) (2024)
- [Parallel Corpora for Machine Translation in Low-resource Indic Languages: A Comprehensive Review](https://mlsanity.com/paper/2503.04797/) (2025)
- [Textless Speech-to-Speech Translation With Limited Parallel Data](https://mlsanity.com/paper/2305.15405/) (2023)
- [Enhancing Out-of-Vocabulary Performance of Indian TTS Systems for Practical Applications through Low-Effort Data Strategies](https://mlsanity.com/paper/2407.13435/) (2024)
- [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)
- [Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties](https://mlsanity.com/paper/2601.04373/) (2026)
- [MunTTS: A Text-to-Speech System for Mundari](https://mlsanity.com/paper/2401.15579/) (2024)
- [Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS](https://mlsanity.com/paper/2410.06608/) (2024)
- [Structured Sparsity and Weight-adaptive Pruning for Memory and Compute efficient Whisper models](https://mlsanity.com/paper/2510.12666/) (2025)
- [Dynamic Multi-Expert Projectors with Stabilized Routing for Multilingual Speech Recognition](https://mlsanity.com/paper/2601.19451/) (2026)
- [Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages](https://mlsanity.com/paper/2604.21481/) (2026)
- [Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost](https://mlsanity.com/paper/2604.25441/) (2026)
- [PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech](https://mlsanity.com/paper/2604.25476/) (2026)
- [SALTTS: Leveraging Self-Supervised Speech Representations for improved Text-to-Speech Synthesis](https://mlsanity.com/paper/2308.01018/) (2023)
- [Enhancing Kurdish Text-to-Speech with Native Corpus Training: A High-Quality WaveGlow Vocoder Approach](https://mlsanity.com/paper/2409.13734/) (2024)
- [BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting](https://mlsanity.com/paper/2502.05729/) (2025)
- [Text to Speech System for Meitei Mayek Script](https://mlsanity.com/paper/2508.06870/) (2025)
- [Bhasha-Rupantarika: Algorithm-Hardware Co-design approach for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2510.10676/) (2025)
- [Enhancing ASR Performance in the Medical Domain for Dravidian Languages](https://mlsanity.com/paper/2604.19797/) (2026)
- [Multilingual Multi-Speaker Unit Vocoders: A Systematic Analysis of Discrete Speech Representations](https://mlsanity.com/paper/2606.06740/) (2026)
- [Towards a Phonology-Informed Evaluation of Multilingual TTS](https://mlsanity.com/paper/2607.01965/) (2026)
- [Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study](https://mlsanity.com/paper/2608.02235/) (2026)
- [BanglaKontho: Closing the Long-Form Gap in Bangla Text-to-Speech](https://mlsanity.com/paper/2609.29146/) (2026)

## Abstract (arXiv)

Deep learning based text-to-speech (TTS) systems have been evolving rapidly with advances in model architectures, training methodologies, and generalization across speakers and languages. However, these advances have not been thoroughly investigated for Indian language speech synthesis. Such investigation is computationally expensive given the number and diversity of Indian languages, relatively lower resource availability, and the diverse set of advances in neural TTS that remain untested. In this paper, we evaluate the choice of acoustic models, vocoders, supplementary loss functions, training schedules, and speaker and language diversity for Dravidian and Indo-Aryan languages. Based on this, we identify monolingual models with FastPitch and HiFi-GAN V1, trained jointly on male and female speakers to perform the best. With this setup, we train and evaluate TTS models for 13 languages and find our models to significantly improve upon existing models in all languages as measured by mean opinion scores. We open-source all models on the Bhashini platform.