TeleASR-Amharic: A Benchmark for Robust Amharic ASR in Realistic Telecommunication Conditions

Key points

• The real-world usability of ASR models remains challenging, particularly for low-resource languages, where existing speech resources are often collected in clean studio environments.1
• We analyze the major degradations observed in real telecommunications recordings and develop a benchmark that covers multiple channel conditions, environmental disturbances, and telecom-domain variations.1
• We further investigate the effects of different noise and channel distortions and show that scaling training data and applying carefully designed telecommunication-aware augmentation substantially improve robustness.1
• Together with the release of our telecommunications-domain data, these findings provide a reproducible benchmark for measuring progress in Amharic ASR while highlighting open challenges in developing robust speech technologies for real-world deployment.1

Sources (1)

• [1]TeleASR-Amharic: A Benchmark for Robust Amharic ASR in Realistic Telecommunication Conditions
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:06 PM

  In this work, we introduce TeleASR-Amharic, a benchmark for evaluating Amharic ASR under various acoustic and telecommunication conditions.

  The real-world usability of ASR models remains challenging, particularly for low-resource languages, where existing speech resources are often collected in clean studio environments.

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