Word Embedding with Emotionally Relevant Keyword Search for Context Detection from Smart Home Voice Commands

Abstract

Voice-enabled virtual assistants have received widespread popularity in smart homes. Adding a context detection feature in voice conversations with virtual assistants can offer a more personalized experience in smart homes such that it maintains awareness of the ongoing conversation and responds appropriately. In this paper, we present a novel word embedding with emotionally relevant keyword search (WERKS) approach for context detection. This WERKS approach makes use of a combination of emotion detection, keyword search, and word embedding for context detection from voice commands and short conversations with virtual assistants. The TPOT classifier was applied over RAVDESS and a custom data set to obtain experimental results, which demonstrated a 15 and 12 percent increase in prediction accuracy of our defined contexts.