Emotion Detection from Speech and Text

Velasco, Mikel de; Justo, Raquel; Antón, Josu; Carrilero, Mikel; Torres, M. Inés

doi:10.21437/iberspeech.2018-15

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…Some kind of Neural Networks are specifically well suited to deal with this problem and given that deep learning is the state of the art in many AI areas, including emotion recognition, a Convolutional Neural Network architecture was designed for this work. Let us note that in [17] a neural network architecture provided promising results when comparing ot to classical Support Vector Machines, for a regression problem over the task related to TV debates.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Contrasting the Emotions identified in Spanish TV debates and in Human-Machine Interactions

Velasco¹,

Justo²,

Letaifa³

et al. 2021

IberSPEECH 2021

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This work is aimed to contrast the similarities and differences for the emotions identified in two very different scenarios: human-to-human interaction on Spanish TV debates and human-machine interaction with a virtual agent in Spanish. To this end we developed a crowd annotation procedure to label the speech signal in terms of both, emotional categories and Valence-Arousal-Dominance models. The analysis of these data showed interesting findings that allowed to profile both the speakers and the task. Then, Convolutional Neural Networks were used for the automatic classification of the emotional samples in both tasks. Experimental results drew up a different human behavior in both tasks and outlined different speaker profiles.

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Section: Experiments and Resultsmentioning

confidence: 99%

Contrasting the Emotions identified in Spanish TV debates and in Human-Machine Interactions

Velasco¹,

Justo²,

Letaifa³

et al. 2021

IberSPEECH 2021

Self Cite

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show abstract

“…In an unweighted voting method, the class predictions of the base-level classifiers are abridged and the class which gets majority votes is selected as the final class. Numerous deep learning architectures have been used in [22] for emotion detection from both speech and text data.…”

Section: Study Of Speech Emotion Detectionmentioning

confidence: 99%

Bangla Speech Emotion Detection using Machine Learning Ensemble Methods

Ayon¹,

Rabbi²,

Habiba³

et al. 2022

Adv. sci. technol. eng. syst. j.

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Emotion is the most important component of being human, and very essential for everyday activities, such as the interaction between people, decision making, and learning. In order to adapt to the COVID-19 pandemic situation, most of the academic institutions relied on online video conferencing platforms to continue educational activities. Due to low bandwidth in many developing countries, educational activities are being mostly carried out through audio interaction. Recognizing an emotion from audio interaction is important when video interaction is limited or unavailable. The literature has documented several studies on detection of emotion in Bangla text and audio speech data. In this paper, ensemble machine learning methods are used to improve the performance of emotion detection from speech data extracted from audio data. The ensemble learning system consists of several base classifiers, each of which is trained with both spontaneous emotional speech and acted emotional speech data. Several trials with different ensemble learning methods are compared to show how these methods can yield an improvement over traditional machine learning method. The experimental results show the accuracy of ensemble learning methods; 84.37% accuracy was achieved using the ensemble learning with bootstrap aggregation and voting method.

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“…Six set of features were explored in this work according to previous experiments carried out with larger sets [26]…”

Section: Exploring Automatic Detection Of Spontaneous Emotions Fmentioning

confidence: 99%

Can Spontaneous Emotions be Detected from Speech on TV Political Debates?

Velasco

Justo

Zorrilla

et al. 2019

2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

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Decoding emotional states from multimodal signals is an increasingly active domain, within the framework of affective computing, which aims to a better understanding of Human-Human Communication as well as to improve Human-Computer Interaction. But the automatic recognition of spontaneous emotions from speech is a very complex task due to the lack of a certainty of the speaker states as well as to the difficulty to identify a variety of emotions in real scenarios. In this work we explore the extent to which emotional states can be decoded from speech signals extracted from TV political debates. The labelling procedure was supported by perception experiments where only a small set of emotions has been identified. In addition, some scaled judgements of valence, arousal and dominance were also provided. In this framework the paper shows meaningful comparisons between both, the dimensional and the categorical models of emotions, which is a new contribution when dealing with spontaneous emotions. To this end Support Vector Machines (SVM) as well as Feedforward Neural Networks (FNN) have been proposed to develop classifiers and predictors. The experimental evaluation over a Spanish corpus has shown the ability of both models to be identified in speech segments by the proposed artificial systems.

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Emotion Detection from Speech and Text

Cited by 10 publications

References 18 publications

Contrasting the Emotions identified in Spanish TV debates and in Human-Machine Interactions

Contrasting the Emotions identified in Spanish TV debates and in Human-Machine Interactions

Bangla Speech Emotion Detection using Machine Learning Ensemble Methods

Can Spontaneous Emotions be Detected from Speech on TV Political Debates?

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