scholarly journals Robust speaker verification by combining MFCC and entrocy in noisy conditions

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.

scholarly journals MFCC AND CMN BASED SPEAKER RECOGNITION IN NOISY ENVIRONMENT

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.

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

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).

Speaker Recognition Systems in the Last Decade – A Survey

2021 ◽  
Vol 39 (1B) ◽  
pp. 30-40
Author(s):  
Ahmed M. Ahmed ◽  
Aliaa K. Hassan
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Clustering Algorithms ◽  
Predictive Coding ◽  
Gaussian Mixture ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Voice Signal ◽  
Automatic Speaker Recognition ◽  
Authentication System

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.

scholarly journals Voice Pathologies Classification Using GMM And SVM Classifiers

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

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

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

<p><span lang="EN-GB">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. </span></p>

Speaker Verification Under Degraded Conditions Using Empirical Mode Decomposition Based Voice Activity Detection Algorithm

2014 ◽  
Vol 23 (4) ◽  
pp. 359-378
Author(s):  
M. S. Rudramurthy ◽  
V. Kamakshi Prasad ◽  
R. Kumaraswamy
Keyword(s):  
Speaker Recognition ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Front End ◽  
Different Types ◽  
Voice Activity

AbstractThe performance of most of the state-of-the-art speaker recognition (SR) systems deteriorates under degraded conditions, owing to mismatch between the training and testing sessions. This study focuses on the front end of the speaker verification (SV) system to reduce the mismatch between training and testing. An adaptive voice activity detection (VAD) algorithm using zero-frequency filter assisted peaking resonator (ZFFPR) was integrated into the front end of the SV system. The performance of this proposed SV system was studied under degraded conditions with 50 selected speakers from the NIST 2003 database. The degraded condition was simulated by adding different types of noises to the original speech utterances. The different types of noises were chosen from the NOISEX-92 database to simulate degraded conditions at signal-to-noise ratio levels from 0 to 20 dB. In this study, widely used 39-dimension Mel frequency cepstral coefficient (MFCC; i.e., 13-dimension MFCCs augmented with 13-dimension velocity and 13-dimension acceleration coefficients) features were used, and Gaussian mixture model–universal background model was used for speaker modeling. The proposed system’s performance was studied against the energy-based VAD used as the front end of the SV system. The proposed SV system showed some encouraging results when EMD-based VAD was used at its front end.

Gender-based speaker recognition from speech signals using GMM model

2019 ◽  
Vol 33 (35) ◽  
pp. 1950438 ◽  
Author(s):  
Manish Gupta ◽  
Shambhu Shankar Bharti ◽  
Suneeta Agarwal
Keyword(s):  
Speaker Recognition ◽  
Speech Signal ◽  
Speech Synthesis ◽  
English Language ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Speech Signals ◽  
Human Beings ◽  
Mel Frequency Cepstral Coefficients ◽  
Single Level

Speech is a convenient medium for communication among human beings. Speaker recognition is a process of automatically recognizing the speaker by processing the information included in the speech signal. In this paper, a new approach is proposed for speaker recognition through speech signal. Here, a two-level approach is proposed. In the first-level, the gender of the speaker is recognized, and in the second-level speaker is recognized based on recognized gender at first-level. After recognizing the gender of the speaker, search space is reduced to half for the second-level as speaker recognition system searches only in a set of speech signals belonging to identified gender. To identify gender, gender-specific features: Mel Frequency Cepstral Coefficients (MFCC) and pitch are used. Speaker is recognized by using speaker specific features: MFCC, Pitch and RASTA-PLP. Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers are used for identifying the gender and recognizing the speaker, respectively. Experiments are performed on speech signals of two databases: “IIT-Madras speech synthesis and recognition” (containing speech samples spoken by eight male and eight female speakers of eight different regions in English language) and “ELSDSR” (containing speech samples spoken by five male and five female in English language). Experimentally, it is observed that by using two-level approach, time taken for speaker recognition is reduced by 30–32% as compared to the approach when speaker is recognized without identifying the gender (single-level approach). The accuracy of speaker recognition in this proposed approach is also improved from 99.7% to 99.9% as compared to single-level approach. It is concluded through the experiments that speech signal of a minimum 1.12 duration (after neglecting silence parts) is sufficient for recognizing the speaker.

Speaker Verification and Identification

2010 ◽  
pp. 264-289
Author(s):  
Minho Jin ◽  
Chang D. Yoo
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Speaker Verification ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Essential Elements ◽  
Verification System ◽  
Biometric Characteristic ◽  
Speaker Modeling

A speaker recognition system verifies or identifies a speaker’s identity based on his/her voice. It is considered as one of the most convenient biometric characteristic for human machine communication. This chapter introduces several speaker recognition systems and examines their performances under various conditions. Speaker recognition can be classified into either speaker verification or speaker identification. Speaker verification aims to verify whether an input speech corresponds to a claimed identity, and speaker identification aims to identify an input speech by selecting one model from a set of enrolled speaker models. Both the speaker verification and identification system consist of three essential elements: feature extraction, speaker modeling, and matching. The feature extraction pertains to extracting essential features from an input speech for speaker recognition. The speaker modeling pertains to probabilistically modeling the feature of the enrolled speakers. The matching pertains to matching the input feature to various speaker models. Speaker modeling techniques including Gaussian mixture model (GMM), hidden Markov model (HMM), and phone n-grams are presented, and in this chapter, their performances are compared under various tasks. Several verification and identification experimental results presented in this chapter indicate that speaker recognition performances are highly dependent on the acoustical environment. A comparative study between human listeners and an automatic speaker verification system is presented, and it indicates that an automatic speaker verification system can outperform human listeners. The applications of speaker recognition are summarized, and finally various obstacles that must be overcome are discussed.

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