Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients

Chenchah, Farah; Lachiri, Zied

doi:10.14569/ijacsa.2015.061119

Cited by 20 publications

(15 citation statements)

References 17 publications

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“…The facial expression is the most popular way to recognize the affective states [7][8] [9]. Also, the human speech [10] [11] and motions or gestures are very used in emotion assessing problem. However, these channels cannot usually identify the real emotional states because it is easy to secret a facial expression or fake a tone of voice [12].…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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Abstract-Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human's feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

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Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

Self Cite

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“…The method presented in this study consists of acoustic features, deep features, pre-trained CNN and SVM combined model. In many studies, acoustic and deep features are used separately [11], [12], [16], [17]. In this study, acoustic and deep features are combined to improve the semantic information of the emotion features in the speech.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…For the classification, K-Nearest Neighbors (K-NN) algorithm is utilized. In [17], four emotional situations are treated to be classifying emotions in speech. For this purpose, features are extracted by utilizing Linear Frequency Cepstral Coefficients (LFCC) and (MFCC) which are the sound features of emotional speech.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features

2020

IEEE Access

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“…The most famous techniques are based on individual physical signs that includes facial expressions, speech, movements, and gestures, etc. Various investigations use facial expressions, human speech, and texts for emotions recognition [3][4][5]. Recognizing emotion with physical signals is not accurate and it is convenient for people to hide their true feelings to alter their voice or to manipulate their face [6].…”

Section: Introductionmentioning

confidence: 99%

A Review on Physiological Signal Based Emotion Detection

Shahzad¹,

Saleem²,

Ahmed³

et al. 2021

AETiC

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Emotions are feelings that are the result of biochemical processes in the body that are influenced by a variety of factors such as one's state of mind, situations, experiences, and surrounding environment. Emotions have an impact on one's ability to think and act. People interact with each other to share their thoughts and feelings. Emotions play a vital role in the field of medicine and can also strengthen the human computer interaction. There are different techniques being used to detect emotions based on facial features, texts, speech, and physiological signals. One of the physiological signal breathing is a parameter which represents an emotion. The rational belief that different breathing habits are correlated with different emotions has expanded the evidence for a connection between breathing and emotion. In this manuscript different recent investigations about the emotion recognition using respiration patterns have been reviewed. The aim of the survey is to sum up the latest technologies and techniques to help researchers develop a global solution for emotional detection system. Various researchers use benchmark datasets and few of them created their own dataset for emotion recognition. It is observed that many investigators used invasive sensors to acquire respiration signals that makes subject uncomfortable and conscious that affects the results. The numbers of subjects involved in the studies reviewed are of the same age and race which is the reason why the results obtained in those studies cannot be applied to diverse population. There is no single global solution exist.

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Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients

Cited by 20 publications

References 17 publications

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

A Novel Approach for Classification of Speech Emotions Based on Deep and Acoustic Features

A Review on Physiological Signal Based Emotion Detection

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