SVM based Emotional Speaker Recognition using MFCC-SDC Features

Mansour, A. Ben; Lachiri, Zied

doi:10.14569/ijacsa.2017.080471

Cited by 14 publications

(14 citation statements)

References 12 publications

(9 reference statements)

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“…Gaussian Mixture Model (GMM) are used as classifier. Asma Mansour et al [7] SR under various emotional states based on the multiclass SVM were implemented using MFCC and MFCC-SDC as features. MFCC-SDC features outperform the conventional MFCC.…”

Section: Related Workmentioning

confidence: 99%

“…MFCC is one of the widely used spectral characteristics in emotional SR that are collection of coefficients that provide information on the shape of the speech signal spectrum [7]. A total of twenty coefficients were used.…”

Section: A Mfccmentioning

confidence: 99%

“…SDC vectors are computed by combining delta-cepstra calculated across multiple speech frames and hence SDC is determined from MFCC [7]. The SDC coefficients are capable of interpreting a speech signal and the information regarding speaker from the signal that lasts for long time [7].…”

Section: J Shifted Delta Cepstra (Sdc)mentioning

confidence: 99%

See 2 more Smart Citations

Spectral Features for Emotional Speaker Recognition

Sandhya¹,

Koolagudi

et al. 2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Speaker recognition in an emotive environment is a bit challenging task because of influence of emotions in a speech. Identifying the speaker from the speech can be done by analyzing the features of the speech signal. In normal conditions, identifying a speaker is not a tedious task. Whereas, identifying the speaker in an emotional environment such as happy, sad, anger, surprise, sarcastic, fear etc. is really challenging, since speech becomes altered under emotions and noise. The spectral features of speech signal include Mel Frequency Cepstral Coefficients(MFCC), Shifted Delta Cepstral Coefficients (SDCC), spectral centroid, spectral roll off, spectral flatness, spectral contrast, spectral bandwidth, chroma-stft, zero crossing rate, root mean square energy, Linear Prediction Cepstral Coefficients (LPCC), spectral subband centroid, Teager energy based MFCC, line spectral frequencies, single frequency cepstral coefficients, formant frequencies, Power Normalized Cepstral Coefficients (PNCC), etc. The features that are extracted from the speech signal are classified using classifiers. Support Vector Machine(SVM), Gaussian Mixture Model, Gaussian Naive Bayes, K-Nearest Neighbour, Random Forest and a simple Neural Network using Keras is used for classification. The important application include security systems in which a person can be identified by biometrics that is voice of the person. The work aims to identify the speaker in an emotional environment using spectral features and classify using any of the classification techniques and to achieve a high speaker recognition rate. Feature combinations can also be used to improve accuracy. The proposed model performed better than most of the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: A Mfccmentioning

confidence: 99%

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Spectral Features for Emotional Speaker Recognition

Sandhya¹,

Koolagudi

et al. 2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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show abstract

“…Asma, Mansour, and ZiedLachiri [6] they highlight a systematic view of identifying the amplifiers under several emotional conditions based on the multi-vector support seed machine (SVM) workbook. Strengthening the performance of the process of recognizing the emotional speaker has received increasing attention in recent years.…”

Section: Related Workmentioning

confidence: 99%

A Speaker Recognition System Using Gaussian Mixture Model, EM Algorithm and K-Means Clustering

Jadhav¹,

Dharwadkar²

2018

IJMECS

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The automated speaker endorsement technique used for recognition of a person by his voice data. The speaker identification is one of the biometric recognition and they were also used in government services, banking services, building security and intelligence services like this applications. The exactness of this system is based on the pre-processing techniques used to select features produced by the voice and to identify the speaker, the speech modeling methods, as well as classifiers, are used. Here, the edges and continuous quality point are eliminated in the normalization process. The Mel-Scale Frequency Cepstral Coefficient is one of the methods to grab features from a wave file of spoken sentences. The Gaussian Mixture Model technique is used and done experiments on MARF (Modular Audio Recognition Framework) framework to increase outcome estimation. We have presented an end pointing elimination in Gaussian selection medium for MFCC.

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“…The acoustic treatment has been used recently in the diagnosis of many diseases. The MFCC for the extraction of cepstral coefficients has been used in the identification of diseases in newborns by Yasmina Kheddache and Chakib Tadj [2] also Takaya Taguchi et al [3] for the major depressive disorder discrimination and for stress recognition from speech Salsabil Besbes and Zied Lachiri work with a multitaper MFCC features [4], whereas Zied Lachiri had also works on emotion recognition [5,6]. Always at the acoustic treatment we found also Nawel SOUISSI and Adnane CHERIF they work on voice disorders identification [7].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

Drissi¹,

Soumaya²,

Nsiri³

et al. 2019

ijacsa

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The aim of this study presented in this paper is to determine the choice of the appropriate wavelet analyzer with the method of extraction of MFCC coefficients for an assistance in the diagnosis of Parkinson's disease. The analysis used is based on a database of 18 healthy and 20 Parkinsonian patients. The suggested processing is based on the transformation of the speech signal by the wavelet transform through testing several sorts of wavelets, extracting Mel Frequency Cepstral Coefficients (MFCC) from the signals, and we apply the support vector machine (SVM) as classifier. The test results reveal that the best recognition rate, which is 86.84%, is obtained by the wavelets of level 2 at 3 rd scale (Daubechie, Symlet, ReverseBior or BiorSpline) combination-MFCC-SVM.

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SVM based Emotional Speaker Recognition using MFCC-SDC Features

Cited by 14 publications

References 12 publications

Spectral Features for Emotional Speaker Recognition

Spectral Features for Emotional Speaker Recognition

A Speaker Recognition System Using Gaussian Mixture Model, EM Algorithm and K-Means Clustering

Diagnosis of Parkinson’s Disease based on Wavelet Transform and Mel Frequency Cepstral Coefficients

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