Abstract
Language detection is the first step in speech recognition systems. It helps these systems to use grammar and semantics of a language in a better way. Due to these reasons, active research is being carried out in language identification. Every language has specific sound patterns, rhythm, tone, nasal features, etc. We have proposed an approach based on Tensor that uses MFCCs for determining the characteristic features of a language that can be used to identify a spoken language. Tensor based algorithms perform quite well for higher dimensions and scale quite well as compared to classic maximum likelihood estimation (MLE) used in latent variable modeling. Also, this approaches does not suffer from slow convergence and require fewer data points for learning. We have conducted language identification experiments on native Indian English and Hindi for some chosen speakers, and an accuracy of around 70% is observed.
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Jain, S., Parabattina, B., Das, P.K. (2020). Speech Signal Analysis for Language Identification Using Tensors. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_25
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