The results demonstrate that the Whisper model can achieve a substantially low WER, even when adapted to an unseen language.
Abstract
This study reports the development of an Automatic Speech Recognition (ASR) system in Mizo, a low-resource language. The development included collecting 17.62 hours of speech data, curating it, and fine-tuning the Mizo ASR system with three Whisper multilingual models and with the SraVaani 1.0 Indic multilingual model. Whisper-large-v3 achieved the lowest conventional WER (18.08%), while morphology-aware evaluation yielded a WER of 7.22%. Zero-shot evaluation of the SraVaani 1.0 Indic multilingual model yielded a WER of 58.27%, while Mizo-specific fine-tuning reduced the conventional WER to 29.45% and the morphology-aware WER to 17.93%. The results demonstrate that the Whisper model can achieve a substantially low WER, even when adapted to an unseen language. In contrast, SraVaani 1.0 supports the Mizo language in its multilingual model; however, fine-tuning with carefully curated Mizo speech data substantially improves its performance.
A preliminary study on the adaptation of Whisper for Automatic Speech Recognition in Baniwa, an indigenous Arawakan language spoken in Brazil, Colombia, and Venezuela, demonstrating that multilingual foundation models can be successfully adapted to extremely low-resource indigenous languages.
Leonardo Duart, T. Fonseca, T. Chacon· 0 citations
Findings indicate that language-specific fine-tuning plays a more critical role than multilingual generalization in achieving accurate ASR for Indonesian and provide practical guidance for deploying ASR systems in low-resource language scenarios.
J. Hebert, Amalia Zahra· Bulletin of Electrical Engin...· 0 citations
SraVaani-1.0 achieves the lowest word error rate (WER) on a large number of language-dataset pairs while remaining competitive with the best-performing systems on high resource while being assessed exclusively on the VAANI benchmark.
Sujith Pulikodan, A. Basu, J. PavanKumar et al.· 1 citation· ⚡1
This paper investigates the effectiveness of pre-trained models like Wav2Vec XLSR-53 and Whisper-Small for developing ASR systems for the Telugu language, addressing the challenge of limited data availability and demonstrating satisfactory results even when fine-tuned on a smaller dataset.
J. Pushparaj, Muzaffar Ahmad Dar, Sri Gani Kaarthikeya Kammula et al.· Frontiers in Artificial Inte...· 0 citations
BuzzASR is introduced, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages, which achieve state-of-the-art CER among open-source systems on 27 of 102 languages on the combined FLEURS and Common Voice test set.
Shivam Singh, Aditya Yadavalli, Catherine Arnett et al.· 0 citations
Automatic speech recognition (ASR) systems often achieve high accuracy for native speech, yet remain less reliable for non-native (L2) accented speech. This gap raises a question about why ASR performs so well on L1 speech. When acoustic cues diverge from expectation, does ASR accommodate L2 speech acoustics, or does i...
Yuanrong Shen, Oishani Bandopadhyay, Sarah C. Creel· Journal of the Acoustical So...· 0 citations
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