Speaker Recognition With Normal and Telephonic Assamese Speech Using I-Vector and Learning-Based Classifier

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Author(s): Mridusmita Sharma (Gauhati University, India), Rituraj Kaushik (Tezpur University, India)and Kandarpa Kumar Sarma (Gauhati University, India)
Copyright: 2017
Pages: 25
Source title: Handbook of Research on Soft Computing and Nature-Inspired Algorithms
Source Author(s)/Editor(s): Shishir K. Shandilya (Bansal Institute of Research and Technology, India), Smita Shandilya (Sagar Institute of Research Technology and Science, India), Kusum Deep (Indian Institute of Technology Roorkee, India)and Atulya K. Nagar (Liverpool Hope University, UK)
DOI: 10.4018/978-1-5225-2128-0.ch008

Keywords: Algorithms / Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Speaker recognition is the task of identifying a person by his/her unique identification features or behavioural characteristics that are included in the speech uttered by the person. Speaker recognition deals with the identity of the speaker. It is a biometric modality which uses the features of the speaker that is influenced by one's individual behaviour as well as the characteristics of the vocal cord. The issue becomes more complex when regional languages are considered. Here, the authors report the design of a speaker recognition system using normal and telephonic Assamese speech for their case study. In their work, the authors have implemented i-vectors as features to generate an optimal feature set and have used the Feed Forward Neural Network for the recognition purpose which gives a fairly high recognition rate.

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Speaker Recognition With Normal and Telephonic Assamese Speech Using I-Vector and Learning-Based Classifier

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