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

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

Mridusmita Sharma (Gauhati University, India), Rituraj Kaushik (Tezpur University, India) and Kandarpa Kumar Sarma (Gauhati University, India)
DOI: 10.4018/978-1-7998-2460-2.ch042

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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Theoretical Considerations

The study of the basic theoretical considerations of speaker recognition and other such related topics gives a proper understanding of the speaker recognition problems. In this section a brief overview of the basic theoretical considerations related to speaker recognition problem, features used and the soft computing technique implemented for the recognition purpose is provided.

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