The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James (1890) first tendered his doctrine that \"the bodily changes follow directly the perception of the exciting fact, and that our feeling of the same changes as they occur is the emotion\" (p. 449). Since we are aware of a variety of feeling and emotion states, it should follow from James' proposition that the various emotions will be accompanied by a variety of differentiable bodily states. Following James' pronouncement, a formidable number of studies were undertaken in search of the physiological differentiators of the emotions. The results, in these early days, were almost uniformly negative. All of the emotional states experi-

"Cognitive, social, and physiological determinants of emotional state": Erratum

Emotion recognition in human-computer interaction

Emotion recognition from speech signals is an important but challenging component of Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER), many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Deep Learning techniques have been recently proposed as an alternative to traditional techniques in SER. This paper presents an overview of Deep Learning techniques and discusses some recent literature where these methods are utilized for speech-based emotion recognition. The review covers databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.

/pdf/speech-emotion-recognition-using-deep-learning-techniques-a-zl8wl0vs5t.pdf

Speech Emotion Recognition Using Deep Learning Techniques: A Review

Speech emotion recognition is a vital and challenging task that the feature extraction plays a significant role in the SER performance. With the development of deep learning, we put our eyes on the structure of end-to-end and authenticate the algorithm that is extraordinary effective. In this paper, we introduce a novel architecture ADRNN (dilated CNN with residual block and BiLSTM based on the attention mechanism) to apply for the speech emotion recognition which can take advantage of the strengths of diverse networks and overcome the shortcomings of utilizing alone, and are evaluated in the popular IEMOCAP database and Berlin EMODB corpus. Dilated CNN can assist the model to acquire more receptive fields than using the pooling layer. Then, the skip connection can keep more historic info from the shallow layer and BiLSTM layer are adopted to learn long-term dependencies from the learned local features. And we utilize the attention mechanism to enhance further extraction of speech features. Furthermore, we improve the loss function to apply softmax together with the center loss that achieves better classification performance. As emotional dialogues are transformed of the spectrograms, we pick up the values of the 3-D Log-Mel spectrums from raw signals and put them into our proposed algorithm and obtain a notable performance to get the 74.96% unweighted accuracy in the speaker-dependent and the 69.32% unweighted accuracy in the speaker-independent experiment. It is better than the 64.74% from previous state-of-the-art methods in the spontaneous emotional speech of the IEMOCAP database. In addition, we propose the networks that achieve recognition accuracies of 90.78% and 85.39% on Berlin EMODB of speaker-dependent and speaker-independent experiment respectively, which are better than the accuracy of 88.30% and 82.82% obtained by previous work. For validating the robustness and generalization, we also make an experiment for cross-corpus between above databases and get the preferable 63.84% recognition accuracy in final.

/pdf/speech-emotion-recognition-from-3d-log-mel-spectrograms-with-fpm0b8dg8e.pdf

Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network

The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition The goal of this study is to provide a survey of the field of discrete speech emotion recognition

https://www.mdpi.com/1424-8220/21/4/1249/pdf

Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models.

Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person's speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.

Databases, features and classifiers for speech emotion recognition: a review

Emotions are broad aspects and expressed in a similar way by every human being; however, these are affected by culture. This creates a major threat to the universality of speech emotion detection system. Cultural behaviour of society affects the way emotions are expressed and perceived. Hence, an emotion recognition system customized for languages within a cultural group is feasible. In this work, a speaker dependent and speaker independent emotion recognition system has been proposed for two different dialects of Odisha: Sambalpuri and Cuttacki. Spectral speech features, such as, log power, Mel-frequency cepstral coefficients (MFCC), Delta MFCC, Double delta MFCC, log frequency power coefficients, and linear predictive cepstral coefficients, are used with Hidden Markov model and support vector machines (SVM) classifier, for classifying a speech into one of the seven discrete emotion classes: anger, happiness, disgust, fear, sadness, surprise, and neutral. For a better comparative study of system’s accuracy, features are taken individually as well as in combinations by varying sampling frequency, frame length and frame overlapping. Best average recognition accuracy obtained for speaker independent system, is 82.14 % for SVM classifier using only MFCC as feature vector. However, for speaker dependent system a hike in accuracy of more than 10 % is seen. It is also revealed that use of MFCC on SVM classifier, not only gives the best overall performance on 8 kHz sampling frequency, but also shows consistent performance for all the emotion classes, compared to other classifiers and feature combinations with less computational complexity. Hence, it can be applied efficiently in call centre application for emotion recognition over telephone.

Study of feature combination using HMM and SVM for multilingual Odiya speech emotion recognition

Differential exponential entropy-based multilevel threshold selection methodology for colour satellite images using equilibrium-cuckoo search optimizer

In this paper a speaker-independent and text-dependent speech emotion recognition system has been presented for the Cuttacki, Sambalpuri and Berhampuri dialects of the Odia language. A dialect is any distinguishable variety of a language spoken by a group of people. Emotions provide naturalness to speech. Here prosodic features are extracted from speech and used for classification of emotions. Prosodic features are represented by pitch, energy, duration, and formant. In order to evaluate the system performance for prosodic features the Orthogonal Forward Selection(OFS) algorithm is used for significant feature selection, and the Gaussian Mixture Model(GMM) and Support Vector Machine(SVM) for classification. The analysis of results, after significant features were found using the OFS algorithm, shows that SVM is a better classification algorithm compared to GMM. The study also shows distinctions between emotions of males and females after feature extractions.

Study of prosodic feature extraction for multidialectal Odia speech emotion recognition

Abstract The Odia language is an old Eastern Indo-Aryan language, spoken by 46.8 million people across India. We have designed an ensemble classifier using Deep Convolutional Recurrent Neural Network for Speech Emotion Recognition (SER). This study presents a new approach for SER tasks motivated by recent research on speech emotion recognition. Initially, we extract utterance-level log Mel-spectrograms and their first and second derivative (Static, Delta, and Delta-delta), represented as 3-D log Mel-spectrograms. We utilize deep convolutional neural networks deep convolutional neural networks to extract the deep features from 3-D log Mel-spectrograms. Then a bi-directional-gated recurrent unit network is applied to express long-term temporal dependency out of all features to produce utterance-level emotion. Finally, we use ensemble classifiers using Softmax and Support Vector Machine classifier to improve the final recognition rate. In this way, our proposed framework is trained and tested on Odia (Seven emotional states) and RAVDESS (Eight emotional states) dataset. The experimental results reveal that an ensemble classifier performs better instead of a single classifier. The accuracy levels reached are 85.31% and 77.54%, outperforming some state-of-the-art frameworks on the Odia and RAVDESS datasets. 

/pdf/a-dcrnn-based-ensemble-classifier-for-speech-emotion-14cndehn.pdf

Monorama Swain

Papers

Databases, features and classifiers for speech emotion recognition: a review

Study of feature combination using HMM and SVM for multilingual Odiya speech emotion recognition

Differential exponential entropy-based multilevel threshold selection methodology for colour satellite images using equilibrium-cuckoo search optimizer

Study of prosodic feature extraction for multidialectal Odia speech emotion recognition

A DCRNN-based ensemble classifier for speech emotion recognition in Odia language