Feature Reduction Method for Speaker Identification Systems Using Particle Swarm Optimization

Abstract

Feature selection (FS) is a process in which the most informative and descriptive characteristics of a signal that will lead to better classification are chosen. The process is utilized in many areas, such as machine learning, pattern recognition and signal processing. FS reduces the dimensionality of a signal and preserves the most informative features for further processing. A speech signal can consist of thousands of features. Feature extraction methods such as Average Framing Linear Prediction Coding (AFLPC) using wavelet transform reduce the number of features from thousands to hundreds. However, the vector of features involves some redundancy. In addition, some features are similar and do not give discrimination to classes. Taking such features into consideration in the classification process will not help to identify certain classes; conversely, they will only serve to confuse the classifier and inhibit identification of accurate classes. This paper proposes an FS method that uses evolution optimization techniques to select the most informative features that maximize the classification rates of Bayesian classifiers. The classification rate is also maximized by modeling the features with the proper number of Gaussian distributions. The results of comparative analysis conducted show that the selection based individual speaker model gives the best classification rate performance.Keyword -Feature Selection, Speaker Identification, Bayes Theorem. I. INTRODUCTIONResearch on automatic speech recognition (ASR) has actively been conductedover the past four decades [1]. ASR is a tool with many potential applications such as automation of operator-assisted services and speech to text systems for hearing-impaired individuals [2]. In speaker recognition systems, the speech signal is represented by several features, which play a major part in system design. Karhunen-Loeve transform (KLT) based features [3], Mel Frequency Cepstral Coefficient (MFCC) [4], Linear Predictive Cepstral Coefficient (LPCC) [5], and wavelet transform-based features [6]-[8] are examples of signal speech features.Various approaches have been proposed to reduce the number of features required for speech recognition. Paliwal[9] reduced the dimensionality of feature vectors in speech recognition systems and tested the technique on four methods. In [10], the Laplacian Eigenmaps Latent Variable Model (LEVLM) used fewer MFCC vectors without affecting the recognition rate, and it exhibited better performance than Principal Component Analysis (PCA). Feature frame selection based on phonetic information has also been investigated to increase classification rate; however, the exact phonemes cannot be easily extracted [11].Joint factor analysis (JFA) [12]- [14]is commonly used to enhance the performance of text independent speaker verification systems by modeling speaker and session variability. This work has been extended to ivector, which outperforms JFA in terms of complexity and model size [15]. The classification rate increaseswith the number of feature...

PCA-WA Based Approach for Concurrent Control Chart Pattern Recognition

“…In the second phase, deep learning neural network was used for testing and learning, and a Monte Carlo simulation was used for verification. Zhao et al (2019) used Principal Component Analysis (PCA) along with a broad learning system for fault diagnosis; ANN along with wavelet and statistical features was used in various other fields as well Zhao et al, 2020;Al-Hmouz et al, 2017;Deng et al, 2019).…”