Robust Assamese Speech Recognition through Controlled Fine-Tuning of Whisper Models
Developing Automatic Speech Recognition (ASR) for morphologically rich, low-resource languages such as Assamese is challenging due to insufficient annotated speech data. The pretrained Whisper model performs poorly on Assamese speech recognition tasks. This paper presents a controlled, fine-tuned Whisper-based Assamese ASR system trained on the Mozilla Common Voice 24.0-Assamese corpus. A hardware-aware optimized training pipeline is implemented for resource-constrained environments, employing mixed-precision training and gradient accumulation on Tesla 4 Graphics Processing Units (T4 GPUs). The proposed fine-tuned model significantly outperformed the Zero-shot baseline, yielding Word Erro...
| Authors | Ganapati Das, Dwipen Laskar, Hasin Afzal Ahmed et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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