Abstract
In this paper, we apply two acoustic models to build the Amazigh speech recognition system; the first system based on hidden Markov models (HMMs) using the open-source CMU Sphinx-4, from the Carnegie Mellon University, the second system based on the convolution neural network CNN is a particular form of neural network implementing in TensorFlow and GPU computation. The two systems evaluated use mel frequency cepstral coefficients to extract the MFFc features. The corpus consists of 9900 audio files. The system obtained the best results when trained using CNN produced 92% of accuracy.
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Telmem, M., Ghanou, Y. (2020). A Comparative Study of HMMs and CNN Acoustic Model in Amazigh Recognition System. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_50
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DOI: https://doi.org/10.1007/978-981-15-0947-6_50
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