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Language Classification and Recognition From Audio Using Deep Belief Network

Language Classification and Recognition From Audio Using Deep Belief Network

Santhi Selvaraj, Raja Sekar J., Amutha S.
Copyright: © 2021 |Pages: 25
ISBN13: 9781799825661|ISBN10: 1799825663|ISBN13 Softcover: 9781799825678|EISBN13: 9781799825685
DOI: 10.4018/978-1-7998-2566-1.ch011
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MLA

Selvaraj, Santhi, et al. "Language Classification and Recognition From Audio Using Deep Belief Network." Challenges and Applications of Data Analytics in Social Perspectives, edited by V. Sathiyamoorthi and Atilla Elci, IGI Global, 2021, pp. 189-213. https://doi.org/10.4018/978-1-7998-2566-1.ch011

APA

Selvaraj, S., J., R. S., & S., A. (2021). Language Classification and Recognition From Audio Using Deep Belief Network. In V. Sathiyamoorthi & A. Elci (Eds.), Challenges and Applications of Data Analytics in Social Perspectives (pp. 189-213). IGI Global. https://doi.org/10.4018/978-1-7998-2566-1.ch011

Chicago

Selvaraj, Santhi, Raja Sekar J., and Amutha S. "Language Classification and Recognition From Audio Using Deep Belief Network." In Challenges and Applications of Data Analytics in Social Perspectives, edited by V. Sathiyamoorthi and Atilla Elci, 189-213. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2566-1.ch011

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Abstract

The main objective is to recognize the chat from social media as spoken language by using deep belief network (DBN). Currently, language classification is one of the main applications of natural language processing, artificial intelligence, and deep learning. Language classification is the process of ascertaining the information being presented in which natural language and recognizing a language from the audio sound. Presently, most language recognition systems are based on hidden Markov models and Gaussian mixture models that support both acoustic and sequential modeling. This chapter presents a DBN-based recognition system in three different languages, namely English, Hindi, and Tamil. The evaluation of languages is performed on the self built recorded database, which extracts the mel-frequency cepstral coefficients features from the speeches. These features are fed into the DBN with a back propagation learning algorithm for the recognition process. Accuracy of the recognition is efficient for the chosen languages and the system performance is assessed on three different languages.

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