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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. [ABSTRACT FROM AUTHOR]

This study presents a new speech emotion recognition (SER) technique using temporal feature stacking and pooling (TFSP). First, Mel-frequency cepstral coefficients, Mel-spectrogram, and emotional silence factor (ESF) are extracted from segmented audio samples. The normalized features are fed into this neural network for training. For final feature representation, the learned features passed through the proposed TFSP framework. Subsequently, a linear support vector machine classifier is employed for emotion classification. It is evident from the confusion matrices that the suggested method can extract emotional content from speech signals efficiently with more unique emotional aspects from commonly confused emotions. According to this study, a shallow neural network can perform as good as the existing deep learning architectures like CNN, RNN, and attention networks. It may be mentioned here that the proposed method also utilises data augmentation by artificially increasing the number of speakers by disrupting the vocal tract length. Furthermore, these highly complex networks employ millions of trainable parameters, resulting in a longer convergence time. The experiments are carried out on four different language speech emotional datasets, the Berlin emotional speech dataset (EmoDB) in German language, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) in North American English, Surrey Audio-Visual Expressed Emotion Database (SAVEE) in British English and a newly constructed MNITJ-Simulated Emotional Hindi speech Database (MNITJ-SEHSD) in the Hindi language. Experimental results on the proposed framework achieved an overall accuracy of 95.09%, 90.20%, 95.50% and 94.67%, on EmoDB, RAVDESS, SAVEE and MNITJ-SEHSD, respectively, at much lesser computational complexity. These findings are compared to the baseline of the three existing architectures on the same databases. Classification accuracy, precision, recall and F1-score are used to validate the developed method. [ABSTRACT FROM AUTHOR]

Emotion is a state which encompasses a variety of physiological phenomena. Classification of emotions has many applications in fields like customer review, product evaluation, national security, etc., thus making it a prominent area of research. The state-of-art methodologies have used either text or audio files to classify emotions which is in contrast to the proposed work which utilizes the Mel-frequency spectrograms. An integrated methodology TiCNN (Tri integrated Convolutional Neural Network) has been proposed for classifying emotions into eight different classes. Three models namely VGG16, VGG19, and a proposed CNN architecture have been integrated and trained on the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset. The proposed integrated TiCNN approach classifies emotions into eight different classes with an accuracy of 93.27%. Precision, recall and F1-Score of 0.93, 0.92 and 0.92 have also been used as metrics to evaluate the performance of the proposed model. Further, for model validation, the efficiency and efficacy of the proposed methodology have been compared and analysed with the EMO-DB (Berlin Database of Emotional Speech) dataset. The proposed TiCNN model gives an accuracy of 92.78% on the EMO-DB dataset. Empirical evaluation of the proposed methodology has been compared with conventional transfer learning models and state-of-the-art methodologies, where it has shown its superiority over others. [ABSTRACT FROM AUTHOR]

Human emotion detection from multiple languages is a very challenging job. In this work, we have used language emotional databases of various languages such as – Ryerson-Audio-Visual database (RAVDESS), Berlin Database (EmoDb) and Italian Database (Emo-Vo) which are in English, German and Italian languages respectively. The proposed model extract MFCC, chroma, Tonnetz, Contrast from the raw audio file, which is further taken as input in the CNN model to identify emotions correctly. We are not using any visual representation of sound only direct from natural sound data. An extensive comparison is made with some of the previous approaches on emotion detection from speech. The experimental result shows that; the proposed model has successfully worked with all the selected databases with higher accuracy. The same also has been tested with the augmented database. We secure 70.46% for RAVDESS, 70.37% Emo-Db and 73.47% for Emo-Vo in the initial database and best model work in the augmented database. However, test with Original test dataset, secured 96.53% in RAVDESS 96.22% in Emo-Db and Emo-Vo 96.11% respectively. Multilingual Emotion detection, a state of art model, has been discussed with an accuracy of 97.89%. The proposed model is a speaker-independent as well as language-independent emotion detection system. [ABSTRACT FROM AUTHOR]

With the widespread use of touch-screen devices, it is more and more convenient for people to draw sketches on screen. This results in the demand for automatically understanding the sketches. Thus, the sketch recognition task becomes more significant than before. To accomplish this task, it is necessary to solve the critical issue of improving the distinction of the sketch features. To this end, we have made efforts in three aspects. First, a novel multi-scale residual block is designed. Compared with the conventional basic residual block, it can better perceive multi-scale information and reduce the number of parameters during training. Second, a hierarchical residual structure is built by stacking multi-scale residual blocks in a specific way. In contrast with the single-level residual structure, the learned features from this structure are more sufficient. Last but not least, the compact triplet-center loss is proposed specifically for the sketch recognition task. It can solve the problem that the triplet-center loss does not fully consider too large intra-class space and too small inter-class space in sketch field. By studying the above modules, a hierarchical residual network as a whole is proposed for sketch recognition and evaluated on Tu-Berlin benchmark thoroughly. The experimental results show that the proposed network outperforms most of baseline methods and it is excellent among non-sequential models at present. [ABSTRACT FROM AUTHOR]

An optimal representation of acoustic features is an ongoing challenge in automatic speech emotion recognition research. In this study, we proposed Cepstral coefficients based on evolutionary filterbanks as emotional features. It is difficult to guarantee that an individual optimized filterbank provides the best representation for emotion classification. Consequently, we employed six HMM-based binary classifiers that used a specific filterbank, which was optimized by a genetic algorithm to categorize the data into seven emotion classes. These optimized classifiers were applied in a hierarchical manner and outperformed conventional Mel Frequency Cepstral Coefficients in terms of overall emotion classification accuracy. The proposed method using evolutionary-based Cepstral coefficients achieved a weighted average recall of 87.29% on the Berlin database while the same approach but using conventional Cepstral features achieved only 79.63%. [ABSTRACT FROM AUTHOR]