Benchmarking Automatic Speech Recognition Models for African Languages

• URL: http://arxiv.org/abs/2512.10968v1
• Date: Sun, 30 Nov 2025 10:21:19 GMT
• Title: Benchmarking Automatic Speech Recognition Models for African Languages
• Authors: Alvin Nahabwe, Sulaiman Kagumire, Denis Musinguzi, Bruno Beijuka, Jonah Mubuuke Kyagaba, Peter Nabende, Andrew Katumba, Joyce Nakatumba-Nabende,
• Abstract summary: We benchmark four state-of-the-art ASR models across 13 African languages.<n>We provide new insights into why models behave differently under varying conditions.<n>This study offers practical and insights into the design of ASR systems for underrepresented languages.
• Score: 0.08662702218563621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic speech recognition (ASR) for African languages remains constrained by limited labeled data and the lack of systematic guidance on model selection, data scaling, and decoding strategies. Large pre-trained systems such as Whisper, XLS-R, MMS, and W2v-BERT have expanded access to ASR technology, but their comparative behavior in African low-resource contexts has not been studied in a unified and systematic way. In this work, we benchmark four state-of-the-art ASR models across 13 African languages, fine-tuning them on progressively larger subsets of transcribed data ranging from 1 to 400 hours. Beyond reporting error rates, we provide new insights into why models behave differently under varying conditions. We show that MMS and W2v-BERT are more data efficient in very low-resource regimes, XLS-R scales more effectively as additional data becomes available, and Whisper demonstrates advantages in mid-resource conditions. We also analyze where external language model decoding yields improvements and identify cases where it plateaus or introduces additional errors, depending on the alignment between acoustic and text resources. By highlighting the interaction between pre-training coverage, model architecture, dataset domain, and resource availability, this study offers practical and insights into the design of ASR systems for underrepresented languages.

Related papers

• Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation [49.82863380286994]
  In-context learning may offer novel ways to adapt Large Language Models for low-resource machine translation.<n>In this study, we explore scaling low-resource machine translation ICL beyond the few-shot setting to thousands of examples with long-context models.<n>Our experiments on Javanese and Sundanese show that gains from additional context saturate quickly and can degrade near the maximum context window.
  arXiv  Detail & Related papers  (2026-02-04T17:02:22Z)
• Efficient ASR for Low-Resource Languages: Leveraging Cross-Lingual Unlabeled Data [5.324230283177818]
  We present a systematic investigation into cross-lingual continuous pretraining for low-resource languages.<n>We construct a 3,000-hour multilingual corpus through a scalable unlabeled data collection pipeline.<n>We employ targeted continual pretraining combined with morphologically-aware tokenization to develop a 300M parameter model that achieves performance comparable to systems 5 times larger.
  arXiv  Detail & Related papers  (2025-12-08T08:16:34Z)
• Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages [1.758729398520438]
  We benchmark the performance of two fine-tuned multilingual ASR models, MMS and XLS-R, on five typologically diverse low-resource languages.<n>Our findings show that MMS is best suited when extremely small amounts of training data are available, whereas XLS-R shows parity performance once training data exceed one hour.
  arXiv  Detail & Related papers  (2025-06-20T19:59:49Z)
• Improving Multilingual Math Reasoning for African Languages [49.27985213689457]
  We conduct experiments to evaluate different combinations of data types (translated versus synthetically generated), training stages (pre-training versus post-training), and other model adaptation configurations.<n>Our experiments focuses on mathematical reasoning tasks, using the Llama 3.1 model family as our base model.
  arXiv  Detail & Related papers  (2025-05-26T11:35:01Z)
• KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization [64.1520245849231]
  This paper presents KIT's submissions to the IWSLT 2025 low-resource track.<n>We develop both cascaded systems, and end-to-end (E2E) Speech Translation systems.<n>Building upon pre-trained models, we fine-tune our systems with different strategies to utilize resources efficiently.
  arXiv  Detail & Related papers  (2025-05-26T08:38:02Z)
• Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages [0.43498389175652036]
  This study integrates traditional and novel language models with fine-tuned Whisper models to raise their performance in less commonly studied languages.<n>We demonstrate substantial improvements in word error rate, particularly in low-resource scenarios.<n>While the integration reliably benefits all model sizes, the extent of improvement varies, highlighting the importance of optimized language model parameters.
  arXiv  Detail & Related papers  (2025-03-30T18:03:52Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition [55.2480439325792]
  Speech Meta In-Context LEarning (SMILE) is an innovative framework that combines meta-learning with speech in-context learning (SICL)<n>We show that SMILE consistently outperforms baseline methods in training-free few-shot multilingual ASR tasks.
  arXiv  Detail & Related papers  (2024-09-16T16:04:16Z)
• Unlocking the Potential of Model Merging for Low-Resource Languages [66.7716891808697]
  Adapting large language models to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT) We propose model merging as an alternative for low-resource languages, combining models with distinct capabilities into a single model without additional training. Experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data.
  arXiv  Detail & Related papers  (2024-07-04T15:14:17Z)
• Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models [48.44820587495038]
  Self-supervised representation learning (SSRL) has demonstrated superior performance than supervised models for tasks including phoneme recognition. Training SSRL models poses a challenge for low-resource languages where sufficient pre-training data may not be available. We propose to use audio augmentation techniques, namely: pitch variation, noise addition, accented target language and other language speech to pre-train SSRL models in a low resource condition and evaluate phoneme recognition.
  arXiv  Detail & Related papers  (2023-09-22T10:09:09Z)
• Advancing African-Accented Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models [2.4654745083407175]
  We propose a new multi-rounds adaptation process that uses uncertainty to automate the annotation process.<n>This novel method streamlines data annotation and strategically selects data samples contributing most to model uncertainty.<n>Our results show that our approach leads to a 27% WER relative average improvement while requiring on average 45% less data than established baselines.
  arXiv  Detail & Related papers  (2023-06-03T13:11:37Z)
• Bootstrap an end-to-end ASR system by multilingual training, transfer learning, text-to-text mapping and synthetic audio [8.510792628268824]
  bootstrapping speech recognition on limited data resources has been an area of active research for long. We investigate here the effectiveness of different strategies to bootstrap an RNN-Transducer based automatic speech recognition (ASR) system in the low resource regime. Our experiments demonstrate that transfer learning from a multilingual model, using a post-ASR text-to-text mapping and synthetic audio deliver additive improvements.
  arXiv  Detail & Related papers  (2020-11-25T13:11:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.