An automatic speech recognition system for isolated Amazigh word using 1D & 2D CNN-LSTM architecture
Tóm tắt
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.
Tài liệu tham khảo
Abakarim, F., & Abenaou, A. (2020). Amazigh isolated word speech recognition system using the adaptive orthogonal transform method. 2020 International conference on intelligent systems and computer vision. https://doi.org/10.1109/ISCV49265.2020.9204291
Abdullah, M., Ahmad, M., & Han, D. (2020). Facial expression recognition in videos: An CNN-LSTM based model for video classification. In 2020 International conference on electronics, information, and communication (ICEIC) (pp. 16–18). IEEE. https://doi.org/10.1109/ICEIC49074.2020.9051332
Ameur, M., Bouhjar, A., Boukhris, F., Boukouss, A., Boumalk, A., Elmedlaoui, M., El Mehdi, I., & Souifi, H. (2004). Initiation à la langue amazighe. El Maârif al Jadida.
Badshah, A. M., Rahim, N., Ullah, N., Ahmad, J., Muhammad, K., Lee, M. Y., Kwon, S., & Baik, S. W. (2019). Deep features-based speech emotion recognition for smart affective services. Multimedia Tools and Applications,78(5), 5571–5589. https://doi.org/10.1007/s11042-017-5292-7
Barkani, F., Hamidi, M., Laaidi, N., Zealouk, O., Satori, H., & Satori, K. (2023). Amazigh speech recognition based on the Kaldi ASR toolkit. International Journal of Information Technology. https://doi.org/10.1007/s41870-023-01354-z
Boukous, A. (1995). Société, langues et cultures au maroc : Enjeux symboliques (8th ed., p. 239). Faculté des Lettres et des Sciences Humaines, Université Mohamed V.
Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2018). A comparison of audio signal preprocessing methods for deep neural networks on music tagging. European Signal Processing Conference,2018, 1870–1874. https://doi.org/10.23919/EUSIPCO.2018.8553106
El Ouahabi, S., Atounti, M., & Bellouki, M. (2017). A database for Amazigh speech recognition research: AMZSRD. In Proceedings of 2017 International conference of cloud computing technologies and applications, CloudTech 2017, 2018-Janua (pp. 1–5). IEEE. https://doi.org/10.1109/CloudTech.2017.8284715
El Ouahabi, S., Atounti, M., & Bellouki, M. (2019a). Amazigh speech recognition using triphone modeling and clustering tree decision. Annals of the University of Craiova Mathematics and Computer Science Series,46(1), 55–65.
El Ouahabi, S., Atounti, M., & Bellouki, M. (2019b). Toward an automatic speech recognition system for Amazigh-tarifit language. International Journal of Speech Technology,22(2), 421–432. https://doi.org/10.1007/s10772-019-09617-6
El Ouahabi, S., Atounti, M., & Bellouki, M. (2020). Optimal parameters selected for automatic recognition of spoken Amazigh digits and letters using hidden Markov model toolkit. International Journal of Speech Technology,23(4), 861–871. https://doi.org/10.1007/s10772-020-09762-3
Essa, Y., Hunt, H. G. P., Gijben, M., & Ajoodha, R. (2022). Deep learning prediction of thunderstorm severity using remote sensing weather data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,15, 4004–4013. https://doi.org/10.1109/JSTARS.2022.3172785
Fadoua, A. A., & Siham, B. (2012). Natural language processing for Amazigh language: Challenges and future directions. Language Technology for Normalisation of Less-Resourced Languages,19, 23.
Hajarolasvadi, N., & Demirel, H. (2019). 3D CNN-based speech emotion recognition using k-means clustering and spectrograms. Entropy,21(5), 479. https://doi.org/10.3390/e21050479
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation,9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Idhssaine, A., & El Kirat, Y. (2021). Amazigh language use, perceptions and revitalisation in morocco: The case of rabat-sale region. Journal of North African Studies,26(3), 465–479. https://doi.org/10.1080/13629387.2019.1690996
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM,60(6), 84–90. https://doi.org/10.1145/3065386
Lee, J., & Tashev, I. (2015). High-level feature representation using recurrent neural network for speech emotion recognition. In Proceedings of the annual conference of the international speech communication association, INTERSPEECH, 2015-Janua (pp. 1537–1540). https://doi.org/10.21437/interspeech.2015-336
Oruh, J., Viriri, S., & Adegun, A. (2022). Long short-term memory recurrent neural network for automatic speech recognition. IEEE Access,10, 30069–30079. https://doi.org/10.1109/ACCESS.2022.3159339
Ouhnini, A., Aksasse, B., & Ouanan, M. (2023). Towards an automatic speech-to-text transcription system: Amazigh language. International Journal of Advanced Computer Science and Applications,14(2), 413–418. https://doi.org/10.14569/IJACSA.2023.0140250
Satori, H., & Elhaoussi, F. (2014). Investigation amazigh speech recognition using CMU tools. International Journal of Speech Technology,17(3), 235–243. https://doi.org/10.1007/s10772-014-9223-y
Telmem, M., & Ghanou, Y. (2020). A comparative study of HMMs and CNN acoustic model in amazigh recognition system. Advances in Intelligent Systems and Computing,1076, 533–540. https://doi.org/10.1007/978-981-15-0947-6_50
Telmem, M., & Ghanou, Y. (2021). The convolutional neural networks for Amazigh speech recognition system. Telkomnika (Telecommunication Computing Electronics and Control),19(2), 515–522. https://doi.org/10.12928/TELKOMNIKA.v19i2.16793
Vankdothu, R., Hameed, M. A., & Fatima, H. (2022). A brain tumor identification and classification using deep learning based on CNN-LSTM method. Computers and Electrical Engineering,101(November 2021), 107960. https://doi.org/10.1016/j.compeleceng.2022.107960
Zealouk, O., Satori, H., Laaidi, N., Hamidi, M., & Satori, K. (2020). Noise effect on Amazigh digits in speech recognition system. International Journal of Speech Technology,23(4), 885–892. https://doi.org/10.1007/s10772-020-09764-1