Emotion Recognition from Speech Signals using Excitation Source and Spectral Features

Choudhury, Akash Roy; Ghosh, Anik; Pandey, Rahul; Barman, Subhas

doi:10.1109/aspcon.2018.8748626

Cited by 15 publications

(9 citation statements)

References 16 publications

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“…They obtained the highest accuracy of 74% after combining the three models. In [13], Sequential Minimal Optimization (SMO) and Random Forest (RF) models together with spectral features have been proposed to perform the emotion recognition task. Moreover, three different types of databases were used to measure the robustness of the proposed model to accomplish the job.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A review of the literature revealed that many studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] are conducted in recognizing the emotion from a human speech by using various methods or models. In some studies, the adopted models were further developed [12,15] or improved via different machine learning techniques for automatic emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

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The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Uddin¹,

Chowdury²,

Khandaker³

et al. 2023

Computers, Materials &Amp; Continua

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Human speech indirectly represents the mental state or emotion of others. The use of Artificial Intelligence (AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech. In this study, we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). About 2800 audio files were extracted from the Toronto emotional speech set (TESS) database for this study. A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data. A total of seven types of emotions; Angry, Disgust, Fear, Happy, Neutral, Pleasant-surprise, and Sad were used in this study. Energy, Fundamental frequency, and Mel Frequency Cepstral Coefficient (MFCC) have been used to extract the emotion features, and these features resulted in 97.5% accuracy in the mixed LSTM + CNN model. This mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from speech. It also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Uddin¹,

Chowdury²,

Khandaker³

et al. 2023

Computers, Materials &Amp; Continua

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show abstract

“…This feature is used for calculating spectral shape, like spectral centroid [ 23 ]. It affords coarse idea of high frequency as well as frequency in which specific quantity of energy is limited.…”

Section: Developed Covid-19 Detection Model Based On Hybrid Optimizationmentioning

confidence: 99%

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost

Dar¹,

Srivastava²,

Lone

2022

Computers in Biology and Medicine

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“…In the short term energy technique, the energy associated with voiced region is higher related to other two regions, where this technique is commonly used for unvoiced, silence and voiced speech classification [24]. In signal processing, the spectral flux features are utilized for estimating the variations in powerspectrum of the speech signal by relating its powerspectrum of one frame with other frames [25]. In addition, the spectral centroid features are utilized for measuring the signal's spectrum characteristics [19].…”

Section: Feature Extractionmentioning

confidence: 99%

MALO-LSTM: Multimodal Sentiment Analysis Using Modified Ant Lion Optimization with Long Short Term Memory Network

2022

IJIES

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In recent times, multimodal sentiment analysis is the most researched topic, due to the availability of huge amount of multimodal content. Generally, multimodal sentiment analysis uses text, audio and visual representations for effective sentiment recognition. The detection of sentiment in the natural language is a tricky process even for humans, so making it automation is more complicated. In this article, the input multimodal data is collected from Surrey Audio-Visual Expressed Emotion (SAVEE) and YouTube datasets, and then hybrid feature extraction is performed to extract feature vectors from the different modalities like textual, audio and visual. The Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) techniques are applied to extract features from textual modality. Further, AlexNet, spectral centroid features, spectral flux features, and short term energy are employed to extract features from the visual and audio modalities. The extracted active feature values are given as the input to the Modified Ant Lion Optimizer (MALO) based Long Short Term Memory (LSTM) network for sentiment classification. In the MALO algorithm, two new processes are performed to select optimal hyper-parameters of LSTM network that improves the training and testing mechanism and reduces the computational complexity. The results with the MALO-LSTM model obtained were 98.62% and 98.81% of accuracy on YouTube and SAVEE datasets, relatively higher than some of the existing methods, the proposed approach provided comparatively better performance.

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Emotion Recognition from Speech Signals using Excitation Source and Spectral Features

Cited by 15 publications

References 16 publications

The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost

MALO-LSTM: Multimodal Sentiment Analysis Using Modified Ant Lion Optimization with Long Short Term Memory Network

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