Deep Neural Networks for Emotion Recognition

Shchetinin, Eugene Yu.; Sevastianov, Leonid A.; Kulyabov, Dmitry S.; Ayrjan, E. A.; Demidova, Anastasia V.

doi:10.1007/978-3-030-66471-8_28

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Algorithms trained on those samples are shown not to be robust when deployed in the real world [19]. There have been works that attempt to address environmental distortions such as reverberation, deamplification, and the background noise at predeployment time [19,37,38,45]. However, they do not confirm at post-deployment stage if their strategies of addressing the realisms work.…”

Section: Emotion Detectionmentioning

confidence: 99%

Integrating Voice-Based Machine Learning Technology into Complex Home Environments

Gao¹,

Jabbour²,

Ko³

et al. 2022

Preprint

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To demonstrate the value of machine learning based smart health technologies, researchers have to deploy their solutions into complex real-world environments with real participants. This gives rise to many, oftentimes unexpected, challenges for creating technology in a lab environment that will work when deployed in real home environments. In other words, like more mature disciplines, we need solutions for what can be done at development time to increase success at deployment time. To illustrate an approach and solutions, we use an example of an ongoing project that is a pipeline of voice based machine learning solutions that detects the anger and verbal conflicts of the participants. For anonymity, we call it the XYZ system. XYZ is a smart health technology because by notifying the participants of their anger, it encourages the participants to better manage their emotions. This is important because being able to recognize one's emotions is the first step to better managing one's anger. XYZ was deployed in 6 homes for 4 months each and monitors the emotion of the caregiver of a dementia patient. In this paper we demonstrate some of the necessary steps to be accomplished during the development stage to increase deployment time success, and show where continued work is still necessary. Note

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Section: Emotion Detectionmentioning

confidence: 99%

Integrating Voice-Based Machine Learning Technology into Complex Home Environments

Gao¹,

Jabbour²,

Ko³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Cardiac Arrhythmia Disorders Detection with Deep Learning Models

Shchetinin

Sevastianov

Demidova

et al. 2022

Communications in Computer and Information Science

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Emotion Recognition Robust to Indoor Environmental Distortions and Non-targeted Emotions Using Out-of-distribution Detection

Gao

Salekin

Rose

et al. 2021

ACM Trans. Comput. Healthcare

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The rapid development of machine learning on acoustic signal processing has resulted in many solutions for detecting emotions from speech. Early works were developed for clean and acted speech and for a fixed set of emotions. Importantly, the datasets and solutions assumed that a person only exhibited one of these emotions. More recent work has continually been adding realism to emotion detection by considering issues such as reverberation, de-amplification, and background noise, but often considering one dataset at a time, and also assuming all emotions are accounted for in the model. We significantly improve realistic considerations for emotion detection by (i) more comprehensively assessing different situations by combining the five common publicly available datasets as one and enhancing the new dataset with data augmentation that considers reverberation and de-amplification, (ii) incorporating 11 typical home noises into the acoustics, and (iii) considering that in real situations a person may be exhibiting many emotions that are not currently of interest and they should not have to fit into a pre-fixed category nor be improperly labeled. Our novel solution combines CNN with out-of-data distribution detection. Our solution increases the situations where emotions can be effectively detected and outperforms a state-of-the-art baseline.

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Deep Neural Networks for Emotion Recognition

Cited by 3 publications

References 10 publications

Integrating Voice-Based Machine Learning Technology into Complex Home Environments

Integrating Voice-Based Machine Learning Technology into Complex Home Environments

Cardiac Arrhythmia Disorders Detection with Deep Learning Models

Emotion Recognition Robust to Indoor Environmental Distortions and Non-targeted Emotions Using Out-of-distribution Detection

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