Emotional speaker recognition in simulated and spontaneous context

Mansour, A. Ben; Lachiri, Zied

doi:10.1109/atsip.2016.7523187

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…MFCC-SDC outperforms traditional MFCC and LPCC. Asma Mansour et al [5] have done emotional SR on instantaneous speech using vocal tract feature such as MFCC, LPCC, Gammatone Frequency Cepstral Coefficients (GFCC), Perceptual Linear Prediction (PLP) and the classification has been performed using HMM. K N R K Raju Alluri et al [6] Analyzed both source feature as well as system features for speaker recognition in emotive environment.…”

Section: Related Workmentioning

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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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%

“…LPCC is a spectral feature which is speaker dependent and is widely used in speaker recognition process [5].…”

Section: Linear Predictive Cepstral Coefficients (Lpcc)mentioning

confidence: 99%

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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“…Jawarkar tested and evaluated the rate of different feature groups such as: "MFCCs, Line Spectral Frequencies (LSFs), Teager energy based Mel Frequency Cepstral Coefficient (TMFCCs), Temporal Energy of Sub-band Cepstral Coefficient (TESBCCs) and their combinations MFCCs-LSFs and TESBCCs-LSFs" in text-independent emotional environments. Mansour et.al [11] considered MFCC-Shifted Delta Coefficient (SDC) features to improve the performance of "speaker recognition system in emotional talking environments". Shahin suggested, executed and assessed a two-phase architecture for identifying speakers utilizing their passionate signals by consolidating a single-phase acknowledgment framework for the recognition of both speaker and emotions based on both HMM and SPHMM as classifiers [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Repeat the process to obtain the convergence, (7) Mixture weights are defined as, (8) Means are given by, (9) Variance is defined as, (10) Speaker set S = {1, 2,…, s} is represented by GMM's λ 1 , λ 2 , …, λ s . Then, the speaker model is defined as, (11) .…”

Section: Gmm Based Estimationmentioning

confidence: 99%

Novel cascaded Gaussian mixture model-deep neural network classifier for speaker identification in emotional talking environments

Shahin

Nassif

Hamsa

2018

Neural Comput & Applic

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This research is an effort to present an effective approach to enhance text-independent speaker identification performance in emotional talking environments based on novel classifier called cascaded Gaussian Mixture Model-Deep Neural Network (GMM-DNN). Our current work focuses on proposing, implementing and evaluating a new approach for speaker identification in emotional talking environments based on cascaded Gaussian Mixture Model-Deep Neural Network as a classifier. The results point out that the cascaded GMM-DNN classifier improves speaker identification performance at various emotions using two distinct speech databases:Emirati speech database (Arabic United Arab Emirates dataset) and "Speech Under Simulated and Actual Stress (SUSAS)" English dataset. The proposed classifier outperforms classical classifiers such as Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in each dataset. Speaker identification performance that has been attained based on the cascaded GMM-DNN is similar to that acquired from subjective assessment by human listeners.2 Keywords: deep neural network; emotional talking environments; Gaussian mixture model; speaker identification. IntroductionSpeaker recognition and its sub divisional entities: "speaker identification and speaker verification" need to be redefined on the basis of talking environments as the neutral talking environment and the emotional talking environments. Speaker recognition performance faces drastic challenges, especially when the speaker identity going through the human-computer interface in emotional talking environments. Speaker recognition applications in security systems are widening their base into banking sector, customer care sector, criminal investigation and can be used as security control measure to remotely access a server or for access to confidential library files on a server. The process of "automatic speaker identification and verification" in stressful and emotional talking environments is a challenging area of research [1]. "Speaker identification" is comprised of two schemes in terms of sets: "closed set" and "open set" speaker identification. When the unknown speaker is presumed to be one among the database of known speakers, it becomes the scheme of a "closed set", while in the scheme of an "open set", the unfamiliar speaker might not necessarily be from the database of familiar speakers. Operational procedure divides speaker identification into "text-dependent", where the same text is uttered by the speaker in the training and testing phases and "text-independent", where different texts are uttered by the speaker during the training and testing phases [2]. 3 A perfect communication from a speaker depends not only on linguistic statements but also on the emotional aspects of the speaker. Identifying the emotional aspects of the speaker by the machine is still a challenge of the human-machine interface. Speech is always a perfect mix of linguistic notes linked with emotion along with its paralinguistic features. Emotion recognition from ...

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The Effects of Social Issues and Human Factors on the Reliability of Biometric Systems: A Review

Azimi

Pacut

2020

Advances in Intelligent Systems and Computing

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Emotional speaker recognition in simulated and spontaneous context

Cited by 3 publications

References 13 publications

Spectral Features for Emotional Speaker Recognition

Spectral Features for Emotional Speaker Recognition

Novel cascaded Gaussian mixture model-deep neural network classifier for speaker identification in emotional talking environments

The Effects of Social Issues and Human Factors on the Reliability of Biometric Systems: A Review

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