Development of High Accuracy Classifier for the Speaker Recognition System

Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this p...

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Bibliographic Details
Published in:Applied bionics and biomechanics 2021, Vol.2021, p.1-10
Main Authors: Al-Hassani, Raghad Tariq, Atilla, Dogu Cagdas, Aydin, Çağatay
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Background noise
Classifiers
Communication
Datasets
Deep learning
Feature recognition
Fourier transforms
Frequency spectrum
Identification systems
Model testing
Neural networks
Noise
Noise levels
Particle swarm optimization
Performance evaluation
Random noise
Speech
Speech recognition
Support vector machines
Time domain analysis
Voice recognition
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Summary:Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), K-nearest neighbour (KNN), and support vector machine (SVM).
ISSN:1176-2322
1754-2103
DOI:10.1155/2021/5559616