An Analysis of the Effectiveness of Synthetic Speech Data for ASR Fine-tuning in Selected Indic Languages
On the map: Speech and audio language models → Vocoders and speech synthesis
Abstract (arXiv)
Synthetic data has the potential to be a valuable resource for training machine learning models, particularly Automatic Speech Recognition (ASR) Systems; however, its effectiveness requires systematic evaluation. In this study, we investigate the impact of incorporating synthetic speech data alongside real-world recordings for three Indic languages: Hindi, Kannada, and Telugu. We analyze the performance gains achieved by augmenting synthetic data with real data and independently examine how ASR performance varies with the sources of scripts used to generate synthetic speech. In addition, we evaluate the effect of synthetic speech generated using different speech synthesis models. Finally, we study the impact of voice cloning in synthetic speech generation on ASR performance, including how performance varies with the number of distinct cloned voices used during data generation.
Key facts. No citing papers in the map yet (data through 19 Oct 2026). Placed in Vocoders and speech synthesis, one of 11 directions in Speech and audio language models. Builds on 16 of its 25 references that are in the map. First posted on arXiv 16 Jun 2026.
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- 2025RASMALAI: Resources for Adaptive Speech Modeling in Indian Languages with Accents and Intonations24 May
- 2024On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures25 Jul
- 2024Natural language guidance of high-fidelity text-to-speech with synthetic annotations2 Feb
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech11 Jun
Other references · 12
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,936
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,596
- 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,462
- 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 810
- 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 539
- 2021Multilingual and code-switching ASR challenges for low resource Indian languagescited by 59
- 2022IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languagescited by 53
- 2020Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systemscited by 51
- 2024IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languagescited by 49
- 2020You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentationcited by 34
- 2022ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversioncited by 15
- 2026VAANI: Capturing the language landscape for an inclusive digital Indiacited by 14
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