Real-time speech emotion analysis for smart home assistants

Chatterjee, R., Mazumdar, S., Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445, Halder, R., Maitra, T. and Giri, D. (2021) Real-time speech emotion analysis for smart home assistants. IEEE Transactions on Consumer Electronics, 67 (1). pp. 68-76. ISSN 0098-3063 doi: 10.1109/TCE.2021.3056421

Abstract/Summary

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 paper, 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% and94.47% 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/96650
Identification Number/DOI	10.1109/TCE.2021.3056421
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	IEEE
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