Speaker Recognition based on Mel-Frequency Cepstral Coefficients and Vector Quantization

2021 ◽  
Author(s):  
Mangesh Balpande ◽  
Rutik Sansare ◽  
Tushar Padelkar ◽  
Vishant Shinde
Keyword(s):  
Vector Quantization ◽  
Speaker Recognition ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

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>

scholarly journals The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool

2019 ◽  
Vol 9 (23) ◽  
pp. 5064 ◽  
Author(s):  
Marco Civera ◽  
Matteo Ferraris ◽  
Rosario Ceravolo ◽  
Cecilia Surace ◽  
Raimondo Betti
Keyword(s):  
Experimental Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Speech Processing ◽  
Speaker Recognition ◽  
Vibration Analysis ◽  
Monitoring Tool ◽  
Mel Frequency Cepstral Coefficients ◽  
Structural Health ◽  
Cepstral Coefficients

Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques for the same aims. The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. These features are very closely related to MFCCs, but provide interesting and useful additional values, such as e.g., improved robustness with respect to noise. The goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. Promising results from both numerical and experimental data were obtained.

scholarly journals Pengenalan Pembicara untuk Menentukan Gender Menggunakan Metode MFCC dan VQ

MIND Journal ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 34-47
Author(s):  
Youllia Indrawaty N ◽  
Andri Ana ◽  
Dita Permatasari
Keyword(s):  
Vector Quantization ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

Klasifikasi suara berdasarkan gender dibuat dengan tujuan agar komputer mampu mengenali suara laki-laki dan perempuan. Dengan kemampuan komputer yang mampu membedakan suara laki-laki dan perempuan pada pengembangan selanjutnya akan memperkuat tingkat suatu sistem keamanan yang menggunakan password dengan suara. Penelitian ini mengenai pengenalan gender dari pengucap/ pembicara dengan ucapan bergantung teks dan bergantung pembicara, dalam proses pengenalan tersebut digunakan algoritma ekstraksi yang disebut Mel Frequency Cepstral Coefficients (MFCC) digunakan untuk ekstraksi ciri dari sinyal wicara sedangkan proses pengelompokan menggunakan metode Vector Quantization (VQ). Dalam tahap pengenalan, ukuran distorsi berdasarkan minimisasi jarak Euclidean digunakan untuk mencocokkan penutur uji dengan penutur dalam database. Database wicara menggunakan 20 penutur, terdiri dari 10 penutur pria dan 10 penutur wanita dengan tingkat akurasi pria mencapai 90% dan wanita 80%.

scholarly journals Gammachirp Filter Banks Applied in Roust Speaker Recognition Based GMM-UBM Classifier

2019 ◽  
Vol 17 (2) ◽  
pp. 170-177
Author(s):  
Lei Deng ◽  
Yong Gao
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Cube Root ◽  
Mel Frequency Cepstral Coefficients ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Auditory Feature ◽  
Cepstral Coefficients

In this paper, authors propose an auditory feature extraction algorithm in order to improve the performance of the speaker recognition system in noisy environments. In this auditory feature extraction algorithm, the Gammachirp filter bank is adapted to simulate the auditory model of human cochlea. In addition, the following three techniques are applied: cube-root compression method, Relative Spectral Filtering Technique (RASTA), and Cepstral Mean and Variance Normalization algorithm (CMVN).Subsequently, based on the theory of Gaussian Mixes Model-Universal Background Model (GMM-UBM), the simulated experiment was conducted. The experimental results implied that speaker recognition systems with the new auditory feature has better robustness and recognition performance compared to Mel-Frequency Cepstral Coefficients(MFCC), Relative Spectral-Perceptual Linear Predictive (RASTA-PLP),Cochlear Filter Cepstral Coefficients (CFCC) and gammatone Frequency Cepstral Coefficeints (GFCC)

scholarly journals SISTEM PENGENALAN PENUTUR DENGAN METODE MEL-FREQUENCY WRAPPING DAN KUANTISASI VEKTOR

MATICS ◽  
2012 ◽  
Author(s):  
Ali Mustofa
Keyword(s):  
Vector Quantization ◽  
Filter Bank ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

<p>Pengenalan penutur adalah proses identifikasi suara seseorang.. Pengenalan penutur  berguna untuk otentikasi biometrik dan komunikasi antara komputer dengan manusia. Teknik Mel Frequency Cepstral Coefficients (MFCC) digunakan untuk  ekstraksi ciri dari sinyal wicara dan membandingkan dengan penutur tak dikenal dengan penutur yang ada dalam database. Filter bank digunakan sebagai pembungkus (wrapping) mel frekuensi. Vector Quantization (VQ) adalah proses meletakkan vektor-vektor ciri yang besar dan menghasilkan ukuran vektor-vektor yang kecil yang berhubungan dengan distribusi centroid. Algoritma  K-mean digunakan untuk kluster. Dalam tahap pengenalan, ukuran distorsi berdasarkan minimisasi jarak Euclidean digunakan untuk mencocokkan penutur tak dikenal dengan penutur dalam database. Database wicara menggunakan 10 penutur berbeda dengan MFCC 12, 20 codebook, dan 16 centroid.</p> <p><em> </em></p> <p><strong>Kata kunci</strong>: penutur, <em>Me</em>l <em>Frequency Cepstral Coefficients, Vector Quantization, K-mean</em></p>

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