A general framework of feature extraction: application to speaker recognition

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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Feature Extraction of Automatic Speaker Recognition, Analysis and Evaluation in Real Environment

Progress in Artificial Intelligence and Pattern Recognition - Lecture Notes in Computer Science ◽

10.1007/978-3-030-01132-1_43 ◽

2018 ◽

pp. 376-383

Author(s):

Edward L. Campbell ◽

Gabriel Hernández ◽

José Ramón Calvo

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Analysis And Evaluation ◽

Real Environment ◽

Automatic Speaker Recognition

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Hardware Implementation of MFCC-Based Feature Extraction for Speaker Recognition

Lecture Notes in Electrical Engineering - Advanced Computer and Communication Engineering Technology ◽

10.1007/978-3-319-07674-4_46 ◽

2014 ◽

pp. 471-480 ◽

Cited By ~ 3

Author(s):

P. Ehkan ◽

F. F. Zakaria ◽

M. N. M. Warip ◽

Z. Sauli ◽

M. Elshaikh

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Hardware Implementation

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A Novel Feature Extraction Methods for Speaker Recognition

Communications in Computer and Information Science - Communications and Information Processing ◽

10.1007/978-3-642-31965-5_83 ◽

2012 ◽

pp. 713-722 ◽

Cited By ~ 1

Author(s):

Muchun Zou

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Extraction Methods

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A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Complexity ◽

10.1155/2020/8810901 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Jiang Lin ◽

Yi Yumei ◽

Zhang Maosheng ◽

Chen Defeng ◽

Wang Chao ◽

...

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Extraction Method ◽

State Of The Art ◽

Recognition System ◽

Nonlinear Dynamic Model ◽

Feature Extraction Method ◽

Analysis Technique ◽

Recognition Systems ◽

Environment Noise

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively.

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Analysis of Distance Measures for Pre-Quantization before Feature Extraction in Automatic Speaker Recognition

2009 Annual IEEE India Conference ◽

10.1109/indcon.2009.5409411 ◽

2009 ◽

Cited By ~ 2

Author(s):

Gourav Sarkar ◽

Goutam Saha

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Distance Measures ◽

Automatic Speaker Recognition

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Feature extraction and modelling techniques for multilingual speaker recognition: a review

International Journal of Signal and Imaging Systems Engineering ◽

10.1504/ijsise.2016.075000 ◽

2016 ◽

Vol 9 (2) ◽

pp. 67

Author(s):

B.G. Nagaraja ◽

H.S. Jayanna

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Modelling Techniques

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Feature extraction and classification techniques for speaker recognition: A review

2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) ◽

10.1109/eesco.2015.7253831 ◽

2015 ◽

Cited By ~ 4

Author(s):

Kinnal Dhameliya ◽

Ninad Bhatt

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Classification Techniques

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Feature Extraction Methods for Speaker Recognition: A Review

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001417500410 ◽

2017 ◽

Vol 31 (12) ◽

pp. 1750041 ◽

Cited By ~ 8

Author(s):

Gopal Chaudhary ◽

Smriti Srivastava ◽

Saurabh Bhardwaj

Keyword(s):

Feature Extraction ◽

Speech Processing ◽

Speaker Recognition ◽

Vocal Tract ◽

Recognition System ◽

Extraction Methods ◽

Future Research ◽

Time Variability ◽

Noise Compensation ◽

Clear Vision

This paper presents main paradigms of research for feature extraction methods to further augment the state of art in speaker recognition (SR) which has been recognized extensively in person identification for security and protection applications. Speaker recognition system (SRS) has become a widely researched topic for the last many decades. The basic concept of feature extraction methods is derived from the biological model of human auditory/vocal tract system. This work provides a classification-oriented review of feature extraction methods for SR over the last 55 years that are proven to be successful and have become the new stone to further research. Broadly, the review work is dichotomized into feature extraction methods with and without noise compensation techniques. Feature extraction methods without noise compensation techniques are divided into following categories: On the basis of high/low level of feature extraction; type of transform; speech production/auditory system; type of feature extraction technique; time variability; speech processing techniques. Further, feature extraction methods with noise compensation techniques are classified into noise-screened features, feature normalization methods, feature compensation methods. This classification-oriented review would endow the clear vision of readers to choose among different techniques and will be helpful in future research in this field.

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A Structured Approach towards Robust Database Collection for Speaker Recognition

Global Journal of Enterprise Information System ◽

10.18311/gjeis/2017/16123 ◽

2017 ◽

Vol 9 (3) ◽

pp. 53 ◽

Cited By ~ 1

Author(s):

Pardeep Sangwan ◽

Saurabh Bhardwaj

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Recognition System ◽

Classification Methods ◽

Extraction Techniques ◽

Recognition Phase ◽

Speech Database ◽

Biometric Systems ◽

Recognition Systems ◽

Structured Approach

Speaker recognition systems are classified according to their database, feature extraction techniques and classification methods. It is analyzed that there is a much need to work upon all the dimensions of forensic speaker recognition systems from the very beginning phase of database collection to recognition phase. The present work provides a structured approach towards developing a robust speech database collection for efficient speaker recognition system. The database required for both systems is entirely different. The databases for biometric systems are readily available while databases for forensic speaker recognition system are scarce. The paper also presents several databases available for speaker recognition systems.

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