# Assessing the Feasibility of Lightweight Whisper Models for Low-Resource Urdu Transcription

arXiv: https://arxiv.org/abs/2508.09865 · HTML: https://mlsanity.com/paper/2508.09865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review](https://mlsanity.com/paper/2211.09511/) (2022)
- [WER We Stand: Benchmarking Urdu ASR Models](https://mlsanity.com/paper/2409.11252/) (2024)
- [Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach](https://mlsanity.com/paper/2406.01446/) (2024)
- [Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling](https://mlsanity.com/paper/2412.16474/) (2024)
- [Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN](https://mlsanity.com/paper/2307.12759/) (2023)
- [From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language](https://mlsanity.com/paper/2411.14493/) (2024)

## Abstract (arXiv)

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.