轻量版Whisper模型在低资源乌尔都语语音识别中表现初显，但仍有明显提升空间。
Assessing the Feasibility of Lightweight Whisper Models for Low-Resource Urdu Transcription
- 直接测试Tiny/Base/Small三个轻量Whisper模型在乌尔都语上的性能
- Whisper-Small效果最好，词错误率仅33.68%，远低于Tiny和Base
- 适合关注低资源语言语音识别的开发者与研究者参考
本研究评估了轻量级Whisper模型（Tiny、Base、Small）在低资源环境下对乌尔都语语音识别的可行性。尽管乌尔都语是全球第十大语言，拥有超过2.3亿使用者，但其在自动语音识别（ASR）系统中的应用仍受限于方言多样性、语言混用及训练数据稀疏等问题。我们在一个精心构建的乌尔都语数据集上进行了无微调的基准测试，采用词错误率（WER）作为评估指标。结果显示，Whisper-Small取得最低错误率（33.68% WER），优于Tiny（67.08% WER）和Base（53.67% WER）。定性分析表明，复杂语句中仍存在音素准确性和词汇连贯性问题。虽然Whisper-Small展现出部署潜力，但仍存在显著差距。研究为未来高效、低资源的乌尔都语ASR系统奠定了基础。
原文摘要 · Abstract (English)
This study evaluates the feasibility of lightweight Whisper models (Tiny, Base, Small) for Urdu speech recognition in low-resource settings. Despite Urdu being the 10th most spoken language globally with over 230 million speakers, its representation in automatic speech recognition (ASR) systems remains limited due to dialectal diversity, code-switching, and sparse training data. We benchmark these models on a curated Urdu dataset using word error rate (WER), without fine-tuning. Results show Whisper-Small achieves the lowest error rates (33.68\% WER), outperforming Tiny (67.08\% WER) and Base (53.67\% WER). Qualitative analysis reveals persistent challenges in phonetic accuracy and lexical coherence, particularly for complex utterances. While Whisper-Small demonstrates promise for deployable Urdu ASR, significant gaps remain. Our findings emphasize lay the groundwork for future research into effective, low-resource ASR systems.
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