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Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm: Deep Learning

Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm: Deep Learning

Neeraja Koppula, K. Sarada, Ibrahim Patel, R. Aamani, K. Saikumar
Copyright: © 2021 |Pages: 12
ISBN13: 9781799868705|ISBN10: 1799868702|ISBN13 Softcover: 9781799868712|EISBN13: 9781799868729
DOI: 10.4018/978-1-7998-6870-5.ch019
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MLA

Koppula, Neeraja, et al. "Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm: Deep Learning." Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies, edited by Jingyuan Zhao and V. Vinoth Kumar, IGI Global, 2021, pp. 278-289. https://doi.org/10.4018/978-1-7998-6870-5.ch019

APA

Koppula, N., Sarada, K., Patel, I., Aamani, R., & Saikumar, K. (2021). Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm: Deep Learning. In J. Zhao & V. Kumar (Eds.), Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies (pp. 278-289). IGI Global. https://doi.org/10.4018/978-1-7998-6870-5.ch019

Chicago

Koppula, Neeraja, et al. "Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm: Deep Learning." In Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies, edited by Jingyuan Zhao and V. Vinoth Kumar, 278-289. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6870-5.ch019

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Abstract

This chapter explains the speech signal in moving objects depending on the recognition field by retrieving the name of individual voice speech and speaker personality. The adequacy of precisely distinguishing a speaker is centred exclusively on vocal features, as voice contact with machines is getting more pervasive in errands like phone, banking exchanges, and the change of information from discourse data sets. This audit shows the location of text-subordinate speakers, which distinguishes a solitary speaker from a known populace. The highlights are eliminated; the discourse signal is enrolled for six speakers. Extraction of the capacity is accomplished utilizing LPC coefficients, AMDF computation, and DFT. By adding certain highlights as information, the neural organization is prepared. For additional correlation, the attributes are put away in models. The qualities that should be characterized for the speakers were acquired and dissected utilizing back propagation algorithm to a format picture.

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