Real-Time Speech Emotion Analysis for Smart Home Assistants

Rajdeep Chatterjee · Saptarshi Mazumdar · Robert Simon Sherratt · Rohit Halder · Tanmoy Maitra · Debasis Giri

133 citations0/0 files storedIEEE Transactions on Consumer Electronics

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Artificial Intelligence (AI) based Speech Emotion Recognition (SER) has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. However, with the increase in computational power, connectivity, and the need to enable people to live in the home for longer though the use of technology, then smart home assistants that could detect human emotion will improve the communication between a user and the assistant enabling the assistant of offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable method considering performance verses complexity for deployment in Consumer Electronics home products, and to present a practical live demonstration of the research. In this article, a comprehensive approach has been introduced for the human speech-based emotion analysis. The 1-D convolutional neural network (CNN) has been implemented to learn and classify the emotions associated with human speech. The paper has been implemented on the standard datasets (emotion classification) Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set database (TESS) (Young and Old). The proposed approach gives 90.48%, 95.79% and 94.47% classification accuracies in the aforementioned datasets. We conclude that the 1-D CNN classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion and are ideal for deployment in smart home assistants to detect emotion.

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References

1992An argument for basic emotions 2003Core affect and the psychological construction of emotion.2018The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English 2017A review of affective computing: From unimodal analysis to multimodal fusion 2012Emotion recognition from speech: a review 2012The relation between valence and arousal in subjective experience.2019Automatic Speech Emotion Recognition Using Machine Learning 2018Reckoning with the Silences of #MeToo

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