Automatic Speech Emotion Recognition: A survey

Chandrasekar, Purnima; Chapaneri, Santosh; Jayaswal, Deepak

doi:10.1109/cscita.2014.6839284

Cited by 28 publications

(11 citation statements)

References 28 publications

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“…Finally, the utterance feature vector is fed to the classifier. There are many classification models that have been used [3], [4], [5], [6], with support vector machine (SVM) being one of the most popular choices.…”

Section: Introductionmentioning

confidence: 99%

Attention Based Fully Convolutional Network for Speech Emotion Recognition

Zhang

Wang

et al. 2018

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

123

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Speech emotion recognition is a challenging task for three main reasons: 1) human emotion is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be detected in some specific moments during a long utterance; 3) speech data with emotional labeling is usually limited. In this paper, we present a novel attention based fully convolutional network for speech emotion recognition. We employ fully convolutional network as it is able to handle variable-length speech, free of the demand of segmentation to keep critical information not lost. The proposed attention mechanism can make our model be aware of which time-frequency region of speech spectrogram is more emotion-relevant. Considering limited data, the transfer learning is also adapted to improve the accuracy. Especially, it's interesting to observe obvious improvement obtained with natural scene image based pre-trained model. Validated on the publicly available IEMOCAP corpus, the proposed model outperformed the state-of-the-art methods with a weighted accuracy of 70.4% and an unweighted accuracy of 63.9% respectively.

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Section: Introductionmentioning

confidence: 99%

Attention Based Fully Convolutional Network for Speech Emotion Recognition

Zhang

Wang

et al. 2018

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

123

View full text Add to dashboard Cite

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“…For the audio modality, the speech features [ 49 , 50 , 51 , 52 , 53 ] include qualitative features, such as voice quality, harshness, tense and breathy; continuous features, such as energy, pitch, formant, zero-cross rate (ZCR), and speech rate; spectral features, such as Mel-frequency cepstral coefficients (MFCC), linear predictor coefficients (LPC), perceptual linear prediction (PLP), and linear predictive cepstral coefficients (LPCC); Teager energy operator (TEO)-based features, such as TEO-decomposed frequency modulation variation (TEO-FM-Var), normalized TEO autocorrelation envelope area (TEO-Auto-Env), and critical band based TEO autocorrelation envelope (TEO-CB-Auto-Env). Similar to the visual modality, given the raw speech signal, researchers first extracted their desired features such as above, then fed them into the classifier.…”

Section: Related Workmentioning

confidence: 99%

Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data during Communication

Quan

Miyake

Nozawa

2021

Sensors

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During social interaction, humans recognize others’ emotions via individual features and interpersonal features. However, most previous automatic emotion recognition techniques only used individual features—they have not tested the importance of interpersonal features. In the present study, we asked whether interpersonal features, especially time-lagged synchronization features, are beneficial to the performance of automatic emotion recognition techniques. We explored this question in the main experiment (speaker-dependent emotion recognition) and supplementary experiment (speaker-independent emotion recognition) by building an individual framework and interpersonal framework in visual, audio, and cross-modality, respectively. Our main experiment results showed that the interpersonal framework outperformed the individual framework in every modality. Our supplementary experiment showed—even for unknown communication pairs—that the interpersonal framework led to a better performance. Therefore, we concluded that interpersonal features are useful to boost the performance of automatic emotion recognition tasks. We hope to raise attention to interpersonal features in this study.

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“…The proposed approach reached the accuracy up to 84.21% in Berlin emotional database. References [26], [27] reviewed several common emotional databases and machine learning-based approaches, such as PCA, Naïve Bayes Classifier, Spectrum method, SVM, regression, etc. The top classification accuracy is up to 90%.…”

Section: A Deep Neural Network and Speech Emotion Recognitionmentioning

confidence: 99%

Enabling Intelligent Environment by the Design of Emotionally Aware Virtual Assistant: A Case of Smart Campus

et al. 2020

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With the advent of the 5G and Artificial Intelligence of Things (AIoT) era, related technologies such as the Internet of Things, big data analysis, cloud applications, and artificial intelligence have brought broad prospects to many application fields, such as smart homes, autonomous vehicles, smart cities, healthcare, and smart campus. At present, most university campus app is presented in the form of static web pages or app menus. This study mainly developed a Deep Neural Network (DNN) based emotionally aware campus virtual assistant. The main contributions of this research are: (1) This study introduces the Chinese Word Embedding to the robot dialogue system, effectively improving dialogue tolerance and semantic interpretation. (2) The traditional method of emotion identification must first tokenize the Chinese sentence, analyze the clauses and part of speech, and capture the emotional keywords before being interpreted by the expert system. Different from the traditional method, this study classifies the input directly through the convolutional neural network after the input sentence is converted into a spectrogram by Fourier Transform. (3) This study is presented in App mode, which is easier to use and economical. (4) This system provides a simple voice response interface, without the need for users to find information in complex web pages or app menus. INDEX TERMS Augmented reality, smart campus, convolutional neural network, recurrent neural network, emotional recognition, chinese word embedding.

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Automatic Speech Emotion Recognition: A survey

Cited by 28 publications

References 28 publications

Attention Based Fully Convolutional Network for Speech Emotion Recognition

Attention Based Fully Convolutional Network for Speech Emotion Recognition

Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data during Communication

Enabling Intelligent Environment by the Design of Emotionally Aware Virtual Assistant: A Case of Smart Campus

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