Context-Aware Voice-Based Interaction in Smart Home - VocADom@A4H Corpus Collection and Empirical Assessment of Its Usefulness

Abstract

Smart homes aim at enhancing the quality of life of people at home by the use of home automation systems and Ambient Intelligence. Most of these smart homes provide enhanced interaction by relying on context-aware systems learned on data. Whereas voice-based interaction is the current emerging trend, most available corpora are either concerned only with home automation sensors or only with audio technology, which limits the development of context-aware voice-based systems. This paper presents the VocADom@A4H corpus, which is a dataset composed of users' interactions recorded in a fully equipped Smart Home. About 12 hours of multichannel distant speech signal synchronized with logs of an openHAB home automation system were collected from 11 participants who performed activities of daily living with the presence of real-life noises, such as other persons speaking, use of vacuum cleaner, TV, etc. This corpus can serve as a valuable material for studies in pervasive intelligence, such as human tracking, human activity recognition, context aware interaction, and robust distant speech processing in the home. Experiments performed on multichannel speech and home automation sensors data for robust voice activity detection and multiresident localization show the potential of the corpus to support the development of context-aware smart home systems.

Exploring the Determinants of Users’ Continuance Usage Intention of Smart Voice Assistants

“…In fact, the voiceassistants have become a central focal point in a smart-home setup as many of the smart-home equipments are voice enabled. Creating automatic routines for home-automation is not only useful and a convenient way of doing certain things, but also opens new opportunities for the users to interact with their voice-assistants [38]. The versatility of the voiceassistants, especially their capability to integrate with a myriad of IoT devices and sensors, for example in a smarthome setup will lead to an effective and productive usage of these devices.…”

End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting

“…Indeed, since our target users are senior adults, they tend to deviate from too strict a grammar (Möller et al, 2008;Takahashi et al, 2003;Vacher et al, 2015), hence the need for a realistic corpus that accounts for the rich set of possible sentences and pronunciations. This corpus is quickly introduced in Section 4.1 and was made available to the community (Portet et al, 2019).…”

Towards End-to-End spoken intent recognition in smart home

“…Hence, we also used the VocADom@A4H corpus [25] which includes about twelve hours of audio signal and was acquired in realistic conditions in the two-storey Amiqual4Home smart home 2 (Fig. 3, 4 and 5).…”