Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions Article Swipe

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Vasudha Kowtha , Vikramjit Mitra , Chris Bartels , Erik Marchi , Sue Booker , William Caruso , Sachin Kajarekar , Devang Naik ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2002.01323

Emotion plays an essential role in human-to-human communication, enabling us to convey feelings such as happiness, frustration, and sincerity. While modern speech technologies rely heavily on speech recognition and natural language understanding for speech content understanding, the investigation of vocal expression is increasingly gaining attention. Key considerations for building robust emotion models include characterizing and improving the extent to which a model, given its training data distribution, is able to generalize to unseen data conditions. This work investigated a long-shot-term memory (LSTM) network and a time convolution - LSTM (TC-LSTM) to detect primitive emotion attributes such as valence, arousal, and dominance, from speech. It was observed that training with multiple datasets and using robust features improved the concordance correlation coefficient (CCC) for valence, by 30\% with respect to the baseline system. Additionally, this work investigated how emotion primitives can be used to detect categorical emotions such as happiness, disgust, contempt, anger, and surprise from neutral speech, and results indicated that arousal, followed by dominance was a better detector of such emotions.

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Disgust Valence (chemistry) Anger Arousal Happiness Emotion classification Cognitive psychology Psychology Contempt Naturalness Categorical variable Utterance Computer science Feeling Speech recognition Social psychology Machine learning Quantum mechanics Physics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2002.01323
PDF: https://arxiv.org/pdf/2002.01323
OA Status: green
Cited By: 1
References: 17
Related Works: 20
OpenAlex ID: https://openalex.org/W3015939301

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DOI: https://doi.org/10.48550/arxiv.2002.01323

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Title: Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-01-31

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Authors: Vasudha Kowtha, Vikramjit Mitra, Chris Bartels, Erik Marchi, Sue Booker, William Caruso, Sachin Kajarekar, Devang Naik

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Landing page: https://arxiv.org/abs/2002.01323

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PDF URL: https://arxiv.org/pdf/2002.01323

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Concepts: Disgust, Valence (chemistry), Anger, Arousal, Happiness, Emotion classification, Cognitive psychology, Psychology, Contempt, Naturalness, Categorical variable, Utterance, Computer science, Feeling, Speech recognition, Social psychology, Machine learning, Quantum mechanics, Physics

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Cited by: 1

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