# BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting

arXiv: https://arxiv.org/abs/2502.05729 · HTML: https://mlsanity.com/paper/2502.05729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 9 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Powerset multi-class cross entropy loss for neural speaker diarization](https://mlsanity.com/paper/2310.13025/) (2023)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation](https://mlsanity.com/paper/2106.03153/) (2021)
- [SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model](https://mlsanity.com/paper/2104.05557/) (2021)
- [VoiceFixer: Toward General Speech Restoration with Neural Vocoder](https://mlsanity.com/paper/2109.13731/) (2021)
- [SpeechBERTScore: Reference-Aware Automatic Evaluation of Speech Generation Leveraging NLP Evaluation Metrics](https://mlsanity.com/paper/2401.16812/) (2024)
- [Towards Building Text-To-Speech Systems for the Next Billion Users](https://mlsanity.com/paper/2211.09536/) (2022)
- [ECAPA2: A Hybrid Neural Network Architecture and Training Strategy for Robust Speaker Embeddings](https://mlsanity.com/paper/2401.08342/) (2024)
- [Transfer Learning from Speech Synthesis to Voice Conversion with Non-Parallel Training Data](https://mlsanity.com/paper/2009.14399/) (2020)
- [Generic Indic Text-to-speech Synthesisers with Rapid Adaptation in an End-to-end Framework](https://mlsanity.com/paper/2006.06971/) (2020)
- [Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition](https://mlsanity.com/paper/2311.03196/) (2023)
- [An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios](https://mlsanity.com/paper/2406.08911/) (2024)
- [Learning to Maximize Speech Quality Directly Using MOS Prediction for Neural Text-to-Speech](https://mlsanity.com/paper/2011.01174/) (2020)

## Abstract (arXiv)

This paper introduces BnTTS (Bangla Text-To-Speech), the first framework for Bangla speaker adaptation-based TTS, designed to bridge the gap in Bangla speech synthesis using minimal training data. Building upon the XTTS architecture, our approach integrates Bangla into a multilingual TTS pipeline, with modifications to account for the phonetic and linguistic characteristics of the language. We pre-train BnTTS on 3.85k hours of Bangla speech dataset with corresponding text labels and evaluate performance in both zero-shot and few-shot settings on our proposed test dataset. Empirical evaluations in few-shot settings show that BnTTS significantly improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech. Compared to state-of-the-art Bangla TTS systems, BnTTS exhibits superior performance in Subjective Mean Opinion Score (SMOS), Naturalness, and Clarity metrics.