Surrey Audio-Visual Expressed Emotion Research Articles

Speech emotion recognition using discriminative dimension reduction by employing a modified quantum-behaved particle swarm optimization algorithm

In recent years, Speech Emotion Recognition (SER) has received considerable attention in affective computing field. In this paper, an improved system for SER is proposed. In the feature extraction step, a hybrid high-dimensional rich feature vector is extracted from both speech signal and glottal-waveform signal using techniques such as MFCC, PLPC, and MVDR. The prosodic features derived from fundamental frequency (f0) contour are also added to this feature vector. The proposed system is based on a holistic approach that employs a modified quantum-behaved particle swarm optimization (QPSO) algorithm (called pQPSO) to estimate both the optimal projection matrix for feature-vector dimension reduction and Gaussian Mixture Model (GMM) classifier parameters. Since the problem parameters are in a limited range and the standard QPSO algorithm performs a search in an infinite range, in this paper, the QPSO is modified in such a way that it uses a truncated probability distribution and makes the search more efficient. The system works in real-time and is evaluated on three standard emotional speech databases Berlin database of emotional speech (EMO-DB), Surrey Audio-Visual Expressed Emotion (SAVEE) and Interactive Emotional Dyadic Motion Capture (IEMOCAP). The proposed method improves the accuracy of the SER system compared to classical methods such as FA, PCA, PPCA, LDA, standard QPSO, wQPSO, and deep neural network, and also outperforms many state-of-the-art recent approaches that use the same datasets.

3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms.

Detecting human intentions and emotions helps improve human–robot interactions. Emotion recognition has been a challenging research direction in the past decade. This paper proposes an emotion recognition system based on analysis of speech signals. Firstly, we split each speech signal into overlapping frames of the same length. Next, we extract an 88-dimensional vector of audio features including Mel Frequency Cepstral Coefficients (MFCC), pitch, and intensity for each of the respective frames. In parallel, the spectrogram of each frame is generated. In the final preprocessing step, by applying k-means clustering on the extracted features of all frames of each audio signal, we select k most discriminant frames, namely keyframes, to summarize the speech signal. Then, the sequence of the corresponding spectrograms of keyframes is encapsulated in a 3D tensor. These tensors are used to train and test a 3D Convolutional Neural network using a 10-fold cross-validation approach. The proposed 3D CNN has two convolutional layers and one fully connected layer. Experiments are conducted on the Surrey Audio-Visual Expressed Emotion (SAVEE), Ryerson Multimedia Laboratory (RML), and eNTERFACE’05 databases. The results are superior to the state-of-the-art methods reported in the literature.

Wavelet based feature combination for recognition of emotions

Abstract In this paper, authors tried to develop reduced combinational features for emotional speech recognition. The spectral/cepstral features like wavelet coefficient, LPCC (linear prediction cepstral coefficient) and MFCC (mel-frequency cepstral coefficient) are used as baseline features. The frequency variation with respect to time has been evaluated properly using wavelet coefficients. Again, MFCC and LPCC features are derived from wavelets, so that the exact information of the emotion has been fetched and are used as features. The features are reduced with vector quantization method and used in radial basis function network (RBFNN) classifier. The feature sets are also combined and tested in the classifier. This piece of work deals with five emotions as angry, fear, happy, disgust and neutral. These are tested for Berlin (EMO-DB) and Surrey Audio-Visual Expressed Emotion (SAVEE) database. The proposed frequency based decomposition and combination choice of features show excellent result and it is exhibited in result section.

Developing crossmodal expression recognition based on a deep neural model.

A robot capable of understanding emotion expressions can increase its own capability of solving problems by using emotion expressions as part of its own decision-making, in a similar way to humans. Evidence shows that the perception of human interaction starts with an innate perception mechanism, where the interaction between different entities is perceived and categorized into two very clear directions: positive or negative. While the person is developing during childhood, the perception evolves and is shaped based on the observation of human interaction, creating the capability to learn different categories of expressions. In the context of human–robot interaction, we propose a model that simulates the innate perception of audio–visual emotion expressions with deep neural networks, that learns new expressions by categorizing them into emotional clusters with a self-organizing layer. The proposed model is evaluated with three different corpora: The Surrey Audio–Visual Expressed Emotion (SAVEE) database, the visual Bi-modal Face and Body benchmark (FABO) database, and the multimodal corpus of the Emotion Recognition in the Wild (EmotiW) challenge. We use these corpora to evaluate the performance of the model to recognize emotional expressions, and compare it to state-of-the-art research.

Understanding and Estimation of Emotional Expression Using Acoustic Analysis of Natural Speech

Emotion expression is an essential function for daily life that can be severely affected some psychological disorders. In this paper we identified seven emotional states anger,surprise,sadness ,happiness,fear,disgust and neutral.The definition of parameters is a crucial step in the development of a system for emotion analysis.The 15 explored features are energy intensity,pitch,standard deviation,jitter,shimmer,autocorrelation,noise to harmonic ration,harmonic to noise ration,energy entropy block,short term energy,zero crossing rate,spectral roll-off,spectral centroid and spectral flux,and formants In this work database used is SAVEE(Surrey audio visual expressed emotion).Results by using different learning methods and estimation is done by using a confidence interval for identified parameters are compared and explained.The overall experimental results reveals that Model 2 and Model 3 give better results than Model 1 using learning methods and estimation shows that most emotions are correctly estimated by using energy intensity and pitch.