Real-time speaker identification system using cepstral features

2016 ◽  
Author(s):  
Monalisha Barik ◽  
Susanta Kumar Sarangi ◽  
Sushanta Kumar Sahu
Keyword(s):  
Real Time ◽  
Speaker Identification ◽  
Identification System ◽  
Cepstral Features

scholarly journals A Novel Approach to Increase the Efficiency of a Multi-lingual Real-time Speaker Identification System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Real Time ◽  
Speech Enhancement ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Cost Effective ◽  
Recognition System ◽  
Identification System ◽  
Novel Approach ◽  
Activity Method ◽  
Voice Activity

Nowadays, the real-time speaker recognition system is very popular due to its cost-effective nature. However, it is a very challenging one to produce a more efficient speaker identification system. In our work, we work on a multi-lingual real-time speaker identification system. We work in a novel way to enhance the efficiency of the said system. We take some real speech signals and use different speech enhancement methods and our proposed voice activity method (VAD) to enhance the efficiency of said system. By doing so, we increase the accuracy of the said system relatively by 2% as compared to existing methods.

scholarly journals Analysis of algorithms and implementation of real time speaker identification system

2021 ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Real Time ◽  
Vector Quantization ◽  
Recognition Accuracy ◽  
Speaker Identification ◽  
Analysis Of Algorithms ◽  
Hidden Markov ◽  
General Purpose ◽  
Identification System

The article describes an implementing a real time speaker identification system by voice for embedded and general purpose computers. A review and analysis of existing speaker identification algorithms are made. The speaker's input speech is recorded in the system, go through the preprocessing stage, extract features and voice parameters for further identification. To recognize the speaker by voice parameters, the Vector quantization and Hidden Markov model algorithms are used. The VQ and HMM algorithms showed recognition accuracy of 96% and 98%, respectively.

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