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Voice-Based Speaker Identification and Verification

Voice-Based Speaker Identification and Verification

Keshav Sinha, Rasha Subhi Hameed, Partha Paul, Karan Pratap Singh
ISBN13: 9781799872580|ISBN10: 1799872580|EISBN13: 9781799872597
DOI: 10.4018/978-1-7998-7258-0.ch016
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MLA

Sinha, Keshav, et al. "Voice-Based Speaker Identification and Verification." Handbook of Research on Knowledge and Organization Systems in Library and Information Science, edited by Barbara Jane Holland, IGI Global, 2021, pp. 288-316. https://doi.org/10.4018/978-1-7998-7258-0.ch016

APA

Sinha, K., Hameed, R. S., Paul, P., & Singh, K. P. (2021). Voice-Based Speaker Identification and Verification. In B. Holland (Ed.), Handbook of Research on Knowledge and Organization Systems in Library and Information Science (pp. 288-316). IGI Global. https://doi.org/10.4018/978-1-7998-7258-0.ch016

Chicago

Sinha, Keshav, et al. "Voice-Based Speaker Identification and Verification." In Handbook of Research on Knowledge and Organization Systems in Library and Information Science, edited by Barbara Jane Holland, 288-316. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7258-0.ch016

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Abstract

In recent years, the advancement in voice-based authentication leads in the field of numerous forensic voice authentication technology. For verification, the speech reference model is collected from various open-source clusters. In this chapter, the primary focus is on automatic speech recognition (ASR) technique which stores and retrieves the data and processes them in a scalable manner. There are the various conventional techniques for speech recognition such as BWT, SVD, and MFCC, but for automatic speech recognition, the efficiency of these conventional recognition techniques degrade. So, to overcome this problem, the authors propose a speech recognition system using E-SVD, D3-MFCC, and dynamic time wrapping (DTW). The speech signal captures its important qualities while discarding the unimportant and distracting features using D3-MFCC.

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