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Amazigh CNN speech recognition system based on Mel spectrogram feature extraction method

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Abstract

The field of speech recognition makes it simpler for humans and machines to engage with speech. Number-oriented communication, such as using a registration code, mobile number, score, or account number, can benefit from speech recognition for digits. This paper presents our Amazigh automatic speech recognition (ASR) experience based on the deep learning approach. The convolutional neural network (CNN) and Mel spectrogram are exploited to evaluate audio samples and produce spectrograms as a part of the deep learning strategy. To attempt the recognition of the Amazigh numerals, we use a database that includes digits ranging from zero to nine collected from 42 native speakers in total, men and women between the ages of 20 and 40. Our experimental results show that spoken digits in Amazigh can be identified with a maximum accuracy of 93.62%, 94% Precision, and 94% Recall.

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Acknowledgements

The authors hereby confirm that all the Figures and Tables in the manuscript are ours. Besides, the Figures and images, which are not ours, have been given permission for re-publication attached with the manuscript.

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Authors and Affiliations

  1. LSI Laboratory, FP Taza, USMBA University, Taza, Morocco

    Hossam Boulal, Mohamed Hamidi, Mustapha Abarkan & Jamal Barkani

  2. Team of Modeling and Scientific Computing, FPN, UMP, Nador, Morocco

    Mohamed Hamidi

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  1. Hossam Boulal
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  2. Mohamed Hamidi
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  3. Mustapha Abarkan
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  4. Jamal Barkani
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Correspondence to Mohamed Hamidi.

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Boulal, H., Hamidi, M., Abarkan, M. et al. Amazigh CNN speech recognition system based on Mel spectrogram feature extraction method. Int J Speech Technol 27, 287–296 (2024). https://doi.org/10.1007/s10772-024-10100-0

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