Journal ArticleDOI
Emotion recognition from speech using global and local prosodic features
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TLDR
The results indicate that, the recognition performance using local Prosodic features is better compared to the performance of global prosodic features.Abstract:
In this paper, global and local prosodic features extracted from sentence, word and syllables are proposed for speech emotion or affect recognition. In this work, duration, pitch, and energy values are used to represent the prosodic information, for recognizing the emotions from speech. Global prosodic features represent the gross statistics such as mean, minimum, maximum, standard deviation, and slope of the prosodic contours. Local prosodic features represent the temporal dynamics in the prosody. In this work, global and local prosodic features are analyzed separately and in combination at different levels for the recognition of emotions. In this study, we have also explored the words and syllables at different positions (initial, middle, and final) separately, to analyze their contribution towards the recognition of emotions. In this paper, all the studies are carried out using simulated Telugu emotion speech corpus (IITKGP-SESC). These results are compared with the results of internationally known Berlin emotion speech corpus (Emo-DB). Support vector machines are used to develop the emotion recognition models. The results indicate that, the recognition performance using local prosodic features is better compared to the performance of global prosodic features. Words in the final position of the sentences, syllables in the final position of the words exhibit more emotion discriminative information compared to the words and syllables present in the other positions.read more
Citations
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Journal ArticleDOI
Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks
TL;DR: This paper proposes to learn affect-salient features for SER using convolutional neural networks (CNN), and shows that this approach leads to stable and robust recognition performance in complex scenes and outperforms several well-established SER features.
Journal ArticleDOI
Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers
Mehmet Berkehan Akçay,Kaya Oguz +1 more
TL;DR: This work defines speech emotion recognition systems as a collection of methodologies that process and classify speech signals to detect the embedded emotions and identified and discussed distinct areas of SER.
Journal ArticleDOI
Databases, features and classifiers for speech emotion recognition: a review
TL;DR: 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.
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Human emotion recognition and analysis in response to audio music using brain signals
TL;DR: It has been evident from results that MLP gives best accuracy to recognize human emotion in response to audio music tracks using hybrid features of brain signals.
Journal ArticleDOI
Deep features-based speech emotion recognition for smart affective services
Abdul Malik Badshah,Nasir Rahim,Noor Ullah,Jamil Ahmad,Khan Muhammad,Mi Young Lee,Soonil Kwon,Sung Wook Baik +7 more
TL;DR: This paper proposes rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features from speech spectrograms using a deep convolutional neural network (CNN) with rectangular kernels.
References
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Proceedings ArticleDOI
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Journal ArticleDOI
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Journal ArticleDOI
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Journal ArticleDOI
Speech emotion recognition using hidden Markov models
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Journal ArticleDOI
Describing the emotional states that are expressed in speech
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