Abstract
In our current life, several applications use voice recognition to transcribe an audio from the speaker to text which may be put to use by industrial-technological machines. This paper presents an algorithm for classifying Arabic fricatives as well as the study of its performance. The algorithm developed in this study makes it possible to classify fricative consonants into three groups: (group 1: / ʕ /, / ɣ /, / ћ /, / χ /, and / h /, group 2: /ʃ / and / Ӡ / and group 3: / sʕ /, / s / and / z /. We used, as acoustic index, the percentage distribution of normalized energy in the speech segments (syllables). Our classification system, developed with Matlab software, present a recognition rate of 85.6%. To evaluate the performances of our algorithm, it was compared with the J48 algorithm implemented in the Weka software. We found that our algorithm presents a good classification of Arabic fricative consonants.
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Elfahm, Y., Mounir, B., Mounir, I., Elmaazouzi, L., Farchi, A. (2022). Arabic Fricative Consonants Characterization According to Places of Articulation. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_6
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DOI: https://doi.org/10.1007/978-3-030-96302-6_6
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