Abstract
The availability of automatic speech recognition systems is crucial in various domains such as communication, healthcare, security, education, etc. However, currently, the existing systems often favor dominant languages such as English, French, Arabic, or Asian languages, leaving under-resourced languages without the consideration they deserve. In this specific context, our work is focused on the Amazigh language, which is widely spoken in North Africa. Our primary objective is to develop an automatic speech recognition system specifically for isolated words, with a particular focus on the Tarifit dialect spoken in the Rif region of Northern Morocco. For the dataset construction, we considered 30 isolated words recorded from 80 diverse speakers, resulting in 2400 audio files. The collected corpus is characterized by its quantity, quality, and variety. Moreover, the dataset serves as a valuable resource for further research and development in the field, supporting the advancement of speech recognition technology for underrepresented languages. For the recognition system, we have chosen the most recent approach in the field of speech recognition, which is a combination of convolutional neural networks and LSTM (CNN-LSTM). For the test, we have evaluated two different architectural models: the 1D CNN LSTM and the 2D CNN LSTM. The experimental results demonstrate a remarkable accuracy rate of over 96% in recognizing spoken words utilizing the 2D CNN LSTM architecture.
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Data availability
The data used in this study are not publicly available due to restrictions imposed by the LSA Laboratory. However, the data can be made available by the corresponding author upon reasonable request.
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Daouad, M., Allah, F.A. & Dadi, E.W. An automatic speech recognition system for isolated Amazigh word using 1D & 2D CNN-LSTM architecture. Int J Speech Technol 26, 775–787 (2023). https://doi.org/10.1007/s10772-023-10054-9
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DOI: https://doi.org/10.1007/s10772-023-10054-9