Abstract
In the last decade speech is a thirsty area of research to the researchers. Man–machine interaction through voice is now making us an efficient and effortless mechanism. In our proposed work of language identification, we have taken the International Institute of Information Technology, Hyderabad (IIIT-H) Indic speech corpus where seven languages have been used and each language has 1000 uttered sentence. Thus, a total of 7000 audio samples have been used in our model of language identification. We have done a pre-processing phase, followed by a pitch and Mel Frequency Cepstral Coefficients (MFCC) feature extraction method and finally a Long Short-Term Memory (LSTM) sequence classification has been used for correct identification of the spoken language and obtained a highest training accuracy of 99.8% for the different hyper-parameters discussed in Sect. 5.
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Paul, B., Phadikar, S., Bera, S. (2021). Indian Regional Spoken Language Identification Using Deep Learning Approach. In: Giri, D., Buyya, R., Ponnusamy, S., De, D., Adamatzky, A., Abawajy, J.H. (eds) Proceedings of the Sixth International Conference on Mathematics and Computing. Advances in Intelligent Systems and Computing, vol 1262. Springer, Singapore. https://doi.org/10.1007/978-981-15-8061-1_21
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DOI: https://doi.org/10.1007/978-981-15-8061-1_21
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