A Robust to Outliers Hidden Markov Model with Application in Text-Dependent Speaker Identification

2007 ◽  
Author(s):  
Sotirios Chatzis ◽  
Theodora Varvarigou
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Speaker Identification ◽  
Hidden Markov

scholarly journals Feature Fusion Based Audio-Visual Speaker Identification Using Hidden Markov Model under Different Lighting Variations

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Abdus Sobhan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Feature Fusion ◽  
Speaker Identification ◽  
Hidden Markov ◽  
Visual Features ◽  
Identification System ◽  
Visual Feature ◽  
Feature Vectors ◽  
Cepstral Coefficients

The aim of the paper is to propose a feature fusion based Audio-Visual Speaker Identification (AVSI) system with varied conditions of illumination environments. Among the different fusion strategies, feature level fusion has been used for the proposed AVSI system where Hidden Markov Model (HMM) is used for learning and classification. Since the feature set contains richer information about the raw biometric data than any other levels, integration at feature level is expected to provide better authentication results. In this paper, both Mel Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) are combined to get the audio feature vectors and Active Shape Model (ASM) based appearance and shape facial features are concatenated to take the visual feature vectors. These combined audio and visual features are used for the feature-fusion. To reduce the dimension of the audio and visual feature vectors, Principal Component Analysis (PCA) method is used. The VALID audio-visual database is used to measure the performance of the proposed system where four different illumination levels of lighting conditions are considered. Experimental results focus on the significance of the proposed audio-visual speaker identification system with various combinations of audio and visual features.

scholarly journals Improvement Of The Text Dependent Speaker Identification System Using Discrete MMM With Cepstral Based Features

1970 ◽  
Vol 6 (2) ◽  
pp. 14-21
Author(s):  
Md Rabiul Islam ◽  
Md Fayzur Rahman ◽  
Muhammad Abdul Goffar Khan
Keyword(s):  
Feature Extraction ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Speaker Identification ◽  
Hidden Markov ◽  
Identification System ◽  
Identification Scheme ◽  
Identification Process ◽  
Identification Rate ◽  
Model Technique

In this paper, an improved strategy for automated text based speaker identification scheme has been proposed. The identification process incorporates the Hidden Markov Model technique. After preprocessing the speech, HMM is used in the learning and identification. Features are extracted by different techniques such as RCC, MFCC, ΔMFCC, ΔΔMFCC, LPC and LPCC which is almost different in each case. The highest identification rate of 93% has been achieved in the close set text dependent speaker identification system. Keywords: Biometric Technologies; Automatic Speaker Identification; Cepstral Coefficients; Feature Extraction; Hidden Markov Model. DOI: http://dx.doi.org/10.3329/diujst.v6i2.9341 DIUJST 2011; 6(2): 14-21

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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