Feature Extraction For Emotion Recognition Research Articles

MFF-SAug: Multi feature fusion with spectrogram augmentation of speech emotion recognition using convolution neural network

The Speech Emotion Recognition (SER) is a complex task because of the feature selections that reflect the emotion from the human speech. The SER plays a vital role and is very challenging in Human-Computer Interaction (HCI). Traditional methods provide inconsistent feature extraction for emotion recognition. The primary motive of this paper is to improve the accuracy of the classification of eight emotions from the human voice. The proposed MFF-SAug research, Enhance the emotion prediction from the speech by Noise Removal, White Noise Injection, and Pitch Tuning. On pre-processed speech signals, the feature extraction techniques Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), and Root Mean Square (RMS) are applied and combined to achieve substantial performance used for emotion recognition. The augmentation applies to the raw speech for a contrastive loss that maximizes agreement between differently augmented samples in the latent space and reconstructs the loss of input representation for better accuracy prediction. A state-of-the-art Convolution Neural Network (CNN) is proposed for enhanced speech representation learning and voice emotion classification. Further, this MFF-SAug method is compared with the CNN + LSTM model. The experimental analysis was carried out using the RAVDESS, CREMA, SAVEE, and TESS datasets. Thus, the classifier achieved a robust representation for speech emotion recognition with an accuracy of 92.6 %, 89.9, 84.9 %, and 99.6 % for RAVDESS, CREMA, SAVEE, and TESS datasets, respectively.

The Effect of Time Window Length on EEG-Based Emotion Recognition.

Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.

Feature Extraction for Emotion Recognition in Speech with Machine Learning Algorithm

Feature Extraction for Emotion Recognition in Speech with Machine Learning Algorithm

Speaker-dependent low-level acoustic feature extraction for emotion recognition

In this paper, accuracy for emotion recognition using low- level acoustic features is investigated. The aim of any speech emotion recognition system is to extract acoustic features that are representative of the emotional state of the speaker. Frequency formants, intensity, and pitch are the low-level features proposed for characterizing four different emotions, anger, happy, sadness, and neutral, using acoustic data. Low- level features describe the acoustic, prosodic, and spectral properties of the speech signal and limit the complexity of emotion recognition systems. An algorithm is designed for characterizing each emotion using the acoustic features. It has been proven that various aspects of a speaker’s physical and emotional state can be identified by speech alone. However, the accuracy of such analyses has not been optimized due to acoustic variabilities such as length and complexity of human speech utterance, gender, speaking styles and speech rate. It has also been found that speaker-dependent systems are more accurate in emotion recognition than that of speaker-independent systems. Since speech emotion recognition is relatively a newer field, the set of most powerful features which can distinguish different emotions is not defined; hence, examining the accuracy of emotion recognition using selected acoustical features is an important task.