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.

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Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i2.pp782-789 ◽

2020 ◽

Vol 18 (2) ◽

pp. 782

Author(s):

Musab T. S. Al-Kaltakchi ◽

Haithem Abd Al-Raheem Taha ◽

Mohanad Abd Shehab ◽

Mohamed A.M. Abdullah

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Speaker Identification ◽

Gaussian Mixture ◽

Identification Accuracy ◽

Identification System ◽

Good Representation ◽

Mel Frequency Cepstral Coefficients ◽

Normalization Methods ◽

Cepstral Coefficients

In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method.

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MFCC AND CMN BASED SPEAKER RECOGNITION IN NOISY ENVIRONMENT

International Journal of Electronics Signals and Systems ◽

10.47893/ijess.2013.1137 ◽

2013 ◽

pp. 48-51

Author(s):

DEBASHISH DEV MISHRA ◽

UTPAL BHATTACHARJEE ◽

SHIKHAR KUMAR SARMA

Keyword(s):

Speaker Recognition ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Training Data ◽

Noisy Environment ◽

Noisy Environments ◽

Mel Frequency Cepstral Coefficients ◽

Automatic Speaker Recognition ◽

Cepstral Mean Normalization ◽

Testing Environments

The performance of automatic speaker recognition (ASR) system degrades drastically in the presence of noise and other distortions, especially when there is a noise level mismatch between the training and testing environments. This paper explores the problem of speaker recognition in noisy conditions, assuming that speech signals are corrupted by noise. A major problem of most speaker recognition systems is their unsatisfactory performance in noisy environments. In this experimental research, we have studied a combination of Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Cepstral Mean Normalization (CMN) techniques for speech enhancement. Our system uses a Gaussian Mixture Models (GMM) classifier and is implemented under MATLAB®7 programming environment. The process involves the use of speaker data for both training and testing. The data used for testing is matched up against a speaker model, which is trained with the training data using GMM modeling. Finally, experiments are carried out to test the new model for ASR given limited training data and with differing levels and types of realistic background noise. The results have demonstrated the robustness of the new system.

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Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

Journal of Intelligent Systems ◽

10.1515/jisys-2014-0140 ◽

2016 ◽

Vol 25 (3) ◽

pp. 387-399

Author(s):

P. Mahesha ◽

D.S. Vinod

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Speaker Recognition ◽

Gaussian Mixture ◽

Modeling Technique ◽

Mel Frequency Cepstral Coefficients ◽

Automatic Speaker Recognition ◽

Word Repetition ◽

Syllable Repetition

AbstractThe classification of dysfluencies is one of the important steps in objective measurement of stuttering disorder. In this work, the focus is on investigating the applicability of automatic speaker recognition (ASR) method for stuttering dysfluency recognition. The system designed for this particular task relies on the Gaussian mixture model (GMM), which is the most widely used probabilistic modeling technique in ASR. The GMM parameters are estimated from Mel frequency cepstral coefficients (MFCCs). This statistical speaker-modeling technique represents the fundamental characteristic sounds of speech signal. Using this model, we build a dysfluency recognizer that is capable of recognizing dysfluencies irrespective of a person as well as what is being said. The performance of the system is evaluated for different types of dysfluencies such as syllable repetition, word repetition, prolongation, and interjection using speech samples from the University College London Archive of Stuttered Speech (UCLASS).

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Genetic Algorithm for Combined Speaker and Speech Recognition using Deep Neural Networks

Journal of Telecommunications and Information Technology ◽

10.26636/jtit.2018.119617 ◽

2018 ◽

Vol 2 ◽

pp. 23-31 ◽

Cited By ~ 1

Author(s):

Gurpreet Kaur ◽

Mohit Srivastava ◽

Amod Kumar

Keyword(s):

Genetic Algorithm ◽

Feature Extraction ◽

Speech Recognition ◽

Speaker Recognition ◽

Linear Prediction ◽

Rate Sensitivity ◽

Second Phase ◽

Linear Predictive Coding ◽

Mel Frequency Cepstral Coefficients ◽

Perceptual Linear Prediction

Huge growth is observed in the speech and speaker recognition ﬁeld due to many artiﬁcial intelligence algorithms being applied. Speech is used to convey messages via the language being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algorithm (GA) for combined speaker and speech recognition, relying on optimized Mel Frequency Cepstral Coeﬃcient (MFCC) speech features, and classiﬁcation is performed using a Deep Neural Network (DNN). In the ﬁrst phase, feature extraction using MFCC is executed. Then, feature optimization is performed using GA. In the second phase training is conducted using DNN. Evaluation and validation of the proposed work model is done by setting a real environment, and eﬃciency is calculated on the basis of such parameters as accuracy, precision rate, recall rate, sensitivity, and speciﬁcity. Also, this paper presents an evaluation of such feature extraction methods as linear predictive coding coeﬃcient (LPCC), perceptual linear prediction (PLP), mel frequency cepstral coefﬁcients (MFCC) and relative spectra ﬁltering (RASTA), with all of them used for combined speaker and speech recognition systems. A comparison of diﬀerent methods based on existing techniques for both clean and noisy environments is made as well.

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Speech Recognition Using MFCC and VQLBG

International Journal of Advances in Applied Sciences ◽

10.11591/ijaas.v4.i4.pp151-156 ◽

2015 ◽

Vol 4 (4) ◽

pp. 151

Author(s):

M. Suman ◽

K. Harish ◽

K. Manoj Kumar ◽

S. Samrajyam

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Feature Matching ◽

Biometric Recognition ◽

Mel Frequency Cepstral Coefficients ◽

Voice Input ◽

Voice Signal ◽

Area Unit ◽

The Voice ◽

Algorithmic Rule

Speaker Recognition is the computing task of confirmatory a user’s claimed identity mistreatment characteristics extracted from their voices. This technique is one of the most helpful and in style biometric recognition techniques in the world particularly connected to areas in that security could be a major concern. It are often used for authentication, police work, rhetorical speaker recognition and variety of connected activities. The method of Speaker recognition consists of two modules particularly feature extraction and have matching. Feature extraction is that the method during which we have a tendency to extract a tiny low quantity of knowledge from the voice signal that will later be used to represent every speaker. Feature matching involves identification of the unknown speaker by scrutiny the extracted options from his/her voice input with those from a collection of identified speakers. Our projected work consists of truncating a recorded voice signal, framing it, passing it through a window perform, conniving the Short Term FFT, extracting its options and Matching it with a hold on guide. Cepstral constant Calculation and Mel frequency Cepstral Coefficients (MFCC) area unit applied for feature extraction purpose.VQLBG (Vector Quantization via Linde-Buzo-Gray) algorithmic rule is used for generating guide and feature matching purpose.

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Voice Pathologies Classification Using GMM And SVM Classifiers

International Journal of Mathematics and Computers in Simulation ◽

10.46300/9102.2021.15.21 ◽

2021 ◽

Vol 15 ◽

pp. 110-114

Author(s):

Amara Fethi ◽

Fezari Mohamed

Keyword(s):

Speaker Recognition ◽

Gaussian Mixture ◽

Polynomial Kernel ◽

Support Vector ◽

Mel Frequency Cepstral Coefficients ◽

Classification Rate ◽

Automatic Speaker Recognition ◽

Statistical Pattern ◽

Voice Pathologies ◽

Sensitivity Specificity

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

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Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service

Sensors ◽

10.3390/s20010021 ◽

2019 ◽

Vol 20 (1) ◽

pp. 21 ◽

Cited By ~ 6

Author(s):

Andrej Zgank

Keyword(s):

Markov Models ◽

Predictive Coding ◽

Gaussian Mixture Models ◽

Audio Signal ◽

Gaussian Mixture ◽

Classification Performance ◽

Learning Approaches ◽

Linear Predictive Coding ◽

Mel Frequency Cepstral Coefficients ◽

Acoustic Classification

Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models’ and Gaussian Mixture Models’ topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.

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Robust speaker verification by combining MFCC and entrocy in noisy conditions

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i4.2957 ◽

2021 ◽

Vol 10 (4) ◽

pp. 2310-2319

Author(s):

Duraid Y. Mohammed ◽

Khamis Al-Karawi ◽

Ahmed Aljuboori

Keyword(s):

Speaker Recognition ◽

Speaker Verification ◽

Gaussian Mixture ◽

Mel Frequency Cepstral Coefficients ◽

Automatic Speaker Recognition ◽

Robust Speaker Recognition ◽

Noisy Conditions ◽

New Feature ◽

Highly Correlated ◽

The Fourier Transform

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.

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Speech Recognition System for Isolated Tamil Words using Random Forest Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1467.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 2431-2435

Keyword(s):

Feature Extraction ◽

Speech Recognition ◽

Random Forest ◽

Vocal Tract ◽

Predictive Coding ◽

Recognition System ◽

Linear Predictive Coding ◽

Mel Frequency Cepstral Coefficients ◽

Speech Corpus ◽

Training Time

ASR is the use of system software and hardware based techniques to identify and process human voice. In this research, Tamil words are analyzed, segmented as syllables, followed by feature extraction and recognition. Syllables are segmented using short term energy and segmentation is done in order to minimize the corpus size. The algorithm for syllable segmentation works by performing the STE function of the continuous speech signal. The proposed approach for speech recognition uses the combination of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC). MFCC features are used to extract a feature vector containing all information about the linguistic message. The LPC affords a robust, dependable and correct technique for estimating the parameters that signify the vocal tract system.LPC features can reduce the bit rate of speech (i.e reducing the measurement of transmitting signal).The combined feature extraction technique will minimize the size of transmitting signal. Then the proposed FE algorithm is evaluated on the speech corpus using the Random forest approach. Random forest is an effective algorithm which can build a reliable training model as its training time is less because the classifier works on the subset of features alone.

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