Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection and computational paralinguistics, owing to their ability to reduce factors of variations, for learning from speech. However, studies have suggested to favor a certain type of convolutional operations when building a deep convolutional neural network for speech applications although there has been promising results using different types of convolutional operations. In this work, we study four types of convolutional operations on different input features for speech emotion recognition under noisy and clean conditions in order to derive a comprehensive understanding. Since affective behavioral information has been shown to reflect temporally varying of mental state and convolutional operation are applied locally in time, all deep neural networks share a deep recurrent sub-network architecture for further temporal modeling. We present detailed quantitative module-wise performance analysis to gain insights into information flows within the proposed architectures. In particular, we demonstrate the interplay of affective information and the other irrelevant information during the progression from one module to another. Finally we show that all of our deep neural networks provide state-of-the-art performance on the eNTERFACE'05 corpus.
Before the present era of deep learning, speech emotion recognition systems prevalently relied on a two-stage training approach, where feature engineering and classifier training were performed separately. Commonly used hand-crafted features include pitch, MFCC, log-Mels and the recommended feature sets from the INTERSPEECH challenges. Support vector machine (SVM) and extreme learning machine (ELM) were two of the most competitive classifiers. For the ease to compare models, @cite_11 summarized the performances by a SVM trained on the INTERSPEECH challenge feature sets over several public corpora. @cite_33 recently proposed a sparse kernel reduced-rank regression (SKRRR) for bimodal emotion recognition from facial expressions and speech, which has achieved one of the state-of-the-art performances on the eNTERFACE'05 @cite_48 corpus.
@cite_48 @cite_33 @cite_11
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In this paper we address the sentence-level multi-modal emotion recognition problem. We formulate the emotion recognition task as a multi-category classification problem and propose an innovative solution based on the automatically generated ensemble of trees with binary support vector machines (SVM) classifiers in the tree nodes. We demonstrate the efficacy of our approach by performing four-way (anger, happiness, sadness, neutral) and five-way (including excitement) emotion recognition on the University of Southern California's Interactive Emotional Motion Capture (USC-IEMOCAP) corpus using combinations of acoustic features, lexical features extracted from automatic speech recognition (ASR) output and visual features extracted from facial markers traced by a motion capture system. The experiments show that the proposed ensemble of trees of binary SVM classifiers outperforms classical multi-way SVM classification with one-vs-one voting scheme and achieves state-of-the-art results for all feature combinations. This paper presents our proposed approach for the second Emotion Recognition in The Wild Challenge. We propose a new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes (HOG_TOP) to represent facial expressions. We also explore the properties of visual features and audio features, and adopt Multiple Kernel Learning (MKL) to find an optimal feature fusion. An SVM with multiple kernels is trained for the facial expression classification. Experimental results demonstrate that our method achieves a promising performance. The overall classification accuracy on the validation set and test set are 40.21 and 45.21 , respectively. This paper presents our contribution to ACM ICMI 2015 Emotion Recognition in the Wild Challenge (EmotiW 2015). We participate in both static facial expression (SFEW) and audio-visual emotion recognition challenges. In both challenges, we use a set of visual descriptors and their early and late fusion schemes. For AFEW, we also exploit a set of popularly used spatio-temporal modeling alternatives and carry out multi-modal fusion. For classification, we employ two least squares regression based learners that are shown to be fast and accurate on former EmotiW Challenge corpora. Specifically, we use Partial Least Squares Regression (PLS) and Kernel Extreme Learning Machines (ELM), which is closely related to Kernel Regularized Least Squares. We use a General Procrustes Analysis (GPA) based alignment for face registration. By employing different alignments, descriptor types, video modeling strategies and classifiers, we diversify learners to improve the final fusion performance. Test set accuracies reached in both challenges are relatively 25 above the respective baselines. Using discriminative classifiers, such as Support Vector Machines (SVMs) in combination with, or as an alternative to, Hidden Markov Models (HMMs) has a number of advantages for difficult speech recognition tasks. For example, the models can make use of additional dependencies in the observation sequences than HMMs provided the appropriate form of kernel is used. However standard SVMs are binary classifiers, and speech is a multi-class problem. Furthermore, to train SVMs to distinguish word pairs requires that each word appears in the training data. This paper examines both of these limitations. Tree-based reduction approaches for multiclass classification are described, as well as some of the issues in applying them to dynamic data, such as speech. To address the training data issues, a simplified version of HMM-based synthesis can be used, which allows data for any word-pair to be generated. These approaches are evaluated on two noise corrupted digit sequence tasks: AURORA 2.0; and actual in-car collected data.