Real-time emotion recognition for sales

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)
64 Downloads (Pure)

Abstract

Positive emotion is a pre-condition to any sales contract. Likewise, the ability to perceive the emotions of a customer impacts sales performance.To support emotional perception in buyer-seller interactions, we propose an audio-visual emotion recognition system that can recognize eight emotions: neutral, calm, sad, happy, angry, fearful, surprised, and disgusted. We reduced noise in audio samples and we applied transfer learning for image feature extraction based on a pre-trained deep neural network VGG16. For emotion recognition, we successfully obtained an audio emotion-recognition accuracy of 62.51% and 68% and video emotion-recognition accuracy of 97.13% and 97.77% on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Surrey Audio-Visual Expressed Emotion (SAVEE) datasets respectively. For the combination of the two models, our proposed merging mechanism without re-training achieved an accuracy of close to 100% on both datasets. Finally, we demonstrated our system for a customer satisfaction use case in a real customer-to-salesperson interaction using audio and video models, achieving an average accuracy of 78%.

Original languageEnglish
Title of host publicationProceedings of 16th International Conference on Mobility, Sensing and Networking (MSN 2020)
PublisherIEEE
Pages584-591
Number of pages8
ISBN (Electronic)9781728199160
DOIs
Publication statusPublished - Apr 2021
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Mobility, Sensing and Networking - Tokyo, Japan
Duration: 17 Dec 202019 Dec 2020
Conference number: 16

Conference

ConferenceInternational Conference on Mobility, Sensing and Networking
Abbreviated titleMSN
Country/TerritoryJapan
CityTokyo
Period17/12/202019/12/2020

Keywords

  • Customer satisfaction
  • Deep learning
  • Emotion recognition
  • Internet of Things
  • Transfer learning

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