Speech Emotion Recognition: Methods and Cases Study

Kerkeni, Leila; Serrestou, Youssef; Mbarki, Mohamed; Raoof, Kosai; Mahjoub, Mohamed Ali

doi:10.5220/0006611601750182

Cited by 40 publications

(30 citation statements)

References 10 publications

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“…As shown in Table 1, SVM classifier yields better results above 81%, with feature combination of MFCC and MS for Berlin database. Our results have improved compared to previous results in [21] because we changed the SVM parameters for each type of features to develop a good model.…”

Section: Results and Analysismentioning

confidence: 70%

“…In previous work [21], we present a system for the recognition of «seven acted emotional states (anger, disgust, fear, joy, sadness, and surprise)». To do that, we extracted the MFCC and MS features and used them to train three different machine learning paradigms (MLR, SVM, and RNN).…”

Section: Introductionmentioning

confidence: 99%

“…• Additionally, a feature selection technique is assessed to obtain good features from the set of features extracted in [21].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Speech Emotion Recognition Using Machine Learning

Kerkeni¹,

Serrestou²,

Mbarki³

et al. 2020

Social Media and Machine Learning

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This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.

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Section: Results and Analysismentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Speech Emotion Recognition Using Machine Learning

Kerkeni¹,

Serrestou²,

Mbarki³

et al. 2020

Social Media and Machine Learning

Self Cite

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show abstract

“…They extracted 16 low-level features and tested their methods over several machine learning algorithms, including artificial neural network (ANN), classification and regression tree (CART), support vector machine (SVM) and K-nearest neighbor (KNN) on berlin emotion speech database (Emo-DB). Kerkeni et al [11] proposed a speech emotion recognition method that extracted MFCC with modulation spectral (MS) features and used recurrent neural network (RNN) learning algorithm to classify seven emotions from Emo-DB and Spanish database. The authors in [15] proposed a speech emotion recognition model where glottis was used for compensation of glottal features.…”

Section: Related Studiesmentioning

confidence: 99%

“…Practical applications of emotion recognition systems can be found in many domains such as audio/video surveillance [6], web-based learning, commercial applications [7], clinical studies, entertainment [8], banking [9], call centers [10], computer games [11] and psychiatric diagnosis [12]. In addition, other real applications include remote tracking of persons in a distressed phase, communication between human and robots, mining sentiments of sport fans and customer care services [13], where emotion is perpetually expressed.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Zvarevashe

Olugbara

2020

Algorithms

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Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition.

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A learning framework of modified deep recurrent neural network for classification and recognition of voice mood

Agarwal

Gupta

2022

Adaptive Control & Signal

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Recognition of human emotions is a basic requirement in many real-time applications. Detection of exact emotions through voice provides relevant information for various purposes. Several computational methods have been employedfor the clear analysis of human emotions. Most of the previous approaches face complexities due to certain drawbacks like degraded signal quality, a requirement of high storage space, increased computational complexity, and deteriorated outcomes of classification accuracy. The proposed work was implemented to gather the accurate classification result of embedded emotions and minimize the computational complexities of MDDTRNN (modified deep duck and traveler recurrent neural network). The proposed work includes four steps: preprocessing, feature extraction, feature selection, and classification. In feature extraction, the spectral and frequency features are extracted using the adopting boosted MFCC (Mel frequency cepstral coefficients) method to improve training speed. In feature selection, the best features are selected using an algorithm of AAVOA (adaptive African vulture optimization algorithm). To provide optimal emotion results, the classification step is undertaken by the MDDTRNN technique. The proposed work shows better classification outcomes of emotions when compared to the existing approaches by holding the accuracy of (95.86%), precision as (93.79%), specificity as (94.28%), sensitivity as (92.89%) and the error rate is attained to be 5.266 in terms of IEMOCAP dataset. The accuracy result (96.27%), precision (94.83%), specificity (93.16%), sensitivity (94%) and the error rate is achieved to be 4.982 in terms of the EMODB dataset.

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Speech Emotion Recognition: Methods and Cases Study

Cited by 40 publications

References 10 publications

Automatic Speech Emotion Recognition Using Machine Learning

Automatic Speech Emotion Recognition Using Machine Learning

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

A learning framework of modified deep recurrent neural network for classification and recognition of voice mood

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