Comparative study to realize an automatic speaker recognition system

In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.

Voice Pathologies Classification Using GMM And SVM Classifiers

In this paper we investigate the proprieties of automatic speaker recognition (ASR) to develop a system for voice pathologies detection, where the model does not correspond to a speaker but it corresponds to group of patients who shares the same diagnostic. One of essential part in this topic is the database (described later), the samples voices (healthy and pathological) are chosen from a German database which contains many diseases, spasmodic dysphonia is proposed for this study. This problematic can be solved by statistical pattern recognition techniques where we have proposed the mel frequency cepstral coefficients (MFCC) to be modeled first, with gaussian mixture model (GMM) massively used in ASR then, they are modeled with support vector machine (SVM). The obtained results are compared in order to evaluate the more preferment classifier. The performance of each method is evaluated in a term of the accuracy, sensitivity, specificity. The best performance is obtained with 12 coefficientsMFCC, energy and second derivate along SVM with a polynomial kernel function, the classification rate is 90% for normal class and 93% for pathological class.This work is developed under MATLAB

Robust speaker verification by combining MFCC and entrocy in noisy conditions

Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.

Speaker Recognition Systems in the Last Decade – A Survey

Speaker Recognition Defined by the process of recognizing a person by his\her voice through specific features that extract from his\her voice signal. An Automatic Speaker recognition (ASP) is a biometric authentication system. In the last decade, many advances in the speaker recognition field have been attained, along with many techniques in feature extraction and modeling phases. In this paper, we present an overview of the most recent works in ASP technology. The study makes an effort to discuss several modeling ASP techniques like Gaussian Mixture Model GMM, Vector Quantization (VQ), and Clustering Algorithms. Also, several feature extraction techniques like Linear Predictive Coding (LPC) and Mel frequency cepstral coefficients (MFCC) are examined. Finally, as a result of this study, we found MFCC and GMM methods could be considered as the most successful techniques in the field of speaker recognition so far.

Towards an objective comparison of feature extraction techniques for automatic speaker recognition systems

A common limitation of the previous comparative studies on speaker-features extraction techniques lies in the fact that the comparison is done independently of the used speaker modeling technique and its parameters. The aim of the present paper is twofold. Firstly, it aims to review the most significant advancements in feature extraction techniques used for automatic speaker recognition. Secondly, it seeks to evaluate and compare the currently dominant ones using an objective comparison methodology that overcomes the various limitations and drawbacks of the previous comparative studies. The results of the carried out experiments underlines the importance of the proposed comparison methodology.