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<p>End-to-end automatic speech recognition (ASR) offers a streamlined alternative to traditional systems that rely on multiple, separately trained language, acoustic, and pronunciation models. In this paper, we present a noise-robust, end-to-end ASR framework tailored specifically to the Amharic language. Our approach integrates a convolutional neural network (CNN), a recurrent neural network (RNN), and Connectionist Temporal Classification (CTC) to directly transcribe speech into text—bypassing the need for labor-intensive dictionary creation. We evaluate our method on a large corpus of 20,000 noisy Amharic utterances, achieving a word error rate (WER) of just 7%. This result underlines the effectiveness of our system in handling challenging acoustic conditions. By reducing complexity and manual overhead, our end-to-end model offers a practical and accurate solution for real-world deployments, with broader implications for developing ASR in other low-resource and noise-prone environments.</p>

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A noise-robust end-to-end framework for Amharic speech recognition

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Yohannes Ayana Ejigu,
  • Tesfa Tegegne Asfaw,
  • Surafel Amsalu Tadesse

摘要

End-to-end automatic speech recognition (ASR) offers a streamlined alternative to traditional systems that rely on multiple, separately trained language, acoustic, and pronunciation models. In this paper, we present a noise-robust, end-to-end ASR framework tailored specifically to the Amharic language. Our approach integrates a convolutional neural network (CNN), a recurrent neural network (RNN), and Connectionist Temporal Classification (CTC) to directly transcribe speech into text—bypassing the need for labor-intensive dictionary creation. We evaluate our method on a large corpus of 20,000 noisy Amharic utterances, achieving a word error rate (WER) of just 7%. This result underlines the effectiveness of our system in handling challenging acoustic conditions. By reducing complexity and manual overhead, our end-to-end model offers a practical and accurate solution for real-world deployments, with broader implications for developing ASR in other low-resource and noise-prone environments.