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An amalgamation of integrated features with DeepSpeech2 architecture and improved spell corrector for improving Gujarati language ASR system

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Abstract

Automatic Speech Recognition systems that convert language into written text have greatly transformed human–machine interaction. Although these systems have achieved results, in languages building accurate and reliable ASR models for low resource languages like Gujarati comes with significant challenges. Gujarati lacks data and linguistic resources, making developing high-performance ASR systems quite difficult. In this paper, we propose an approach to enhance the effectiveness of a Gujarati ASR model despite resources. We achieve this by incorporating integrated features such as Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) utilizing the DeepSpeech2 architecture and implementing an improved spell correction technique based on the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Our approach has demonstrated superiority over previous state-of-the-art methodologies through testing and evaluation. The experimental results demonstrate that our proposed method consistently reduces the Word Error Rate (WER) by 10–12 percentage points compared to the existing work, surpassing the most significant improvement of 5.87%. Our findings demonstrate the viability of developing accurate and dependable ASR systems for languages with limited resources, such as Gujarati.

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Authors and Affiliations

  1. Department of Computer Engineering, National Institute of Technology, Kurukshetra, India

    Mohit Dua & Bhavesh Bhagat

  2. Department of Electronics Communication and Engineering, Punjab Engineering College, Chandigarh, India

    Shelza Dua

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  1. Mohit Dua
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  2. Bhavesh Bhagat
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  3. Shelza Dua
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Correspondence to Shelza Dua.

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I, Mohit Dua, on the behalf of all the authors declare that: this study did not receive any finding from any resource, all the authors and the submitted manuscript do not have any conflict of interest and this article does not contain any studies with human participants or animal performed by any of the authors.

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Dua, M., Bhagat, B. & Dua, S. An amalgamation of integrated features with DeepSpeech2 architecture and improved spell corrector for improving Gujarati language ASR system. Int J Speech Technol (2024). https://doi.org/10.1007/s10772-024-10082-z

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