CIRDO: Smart companion for helping elderly to live at home for longer

Bouakaz, Saïda; Vacher, Michel; Chaumon, Marc-Éric Bobillier; Aman, Frédéric; Bekkadja, S.; Portet, François; Guillou, Erwan; Rossato, Solange; Desserée, Élodie; Traineau, P.; Vimont, J.-P.; Chevalier, Thierry Le

doi:10.1016/j.irbm.2014.02.011

Cited by 27 publications

(30 citation statements)

References 18 publications

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“…From this table, it can be seen that very few systems were implemented in realistic smart homes and tested with real users in such SWEET-HOME S9 [Gemmeke et al 2013] ALADIN S10 [Ravanelli and Omologo 2014] DIRHA S11 [Casanueva et al 2014] PIPIN HOMESERVICE S12 [Bouakaz et al 2014] CIRDO S13…”

Section: Voice User Interface In Smart Homesmentioning

confidence: 99%

Evaluation of a Context-Aware Voice Interface for Ambient Assisted Living

Vacher

Caffiau

Portet

et al. 2015

ACM Trans. Access. Comput.

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This article presents an experiment with seniors and people with visual impairment in a voice-controlled smart home using the SWEET-HOME system. The experiment shows some weaknesses in automatic speech recognition that must be addressed, as well as the need for better adaptation to the user and the environment. Users were disturbed by the rigid structure of the grammar and were eager to adapt it to their own preferences. Surprisingly, while no humanoid aspect was introduced in the system, the senior participants were inclined to embody the system. Despite these aspects to improve, the system has been favorably assessed as diminishing most participant fears related to the loss of autonomy. . 2015. Evaluation of a context-aware voice interface for Ambient Assisted Living: qualitative user study vs. quantitative system evaluation.

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Section: Voice User Interface In Smart Homesmentioning

confidence: 99%

Evaluation of a Context-Aware Voice Interface for Ambient Assisted Living

Vacher

Caffiau

Portet

et al. 2015

ACM Trans. Access. Comput.

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show abstract

“…But, despite the interest presented by sound analysis techniques, the use of ASR for voice command for home automation in a real environment is still an open challenge. Voice-User Interface in domestic environment has recently gained interest in the speech processing community as exemplified by the rising number of smart home projects that considers Automatic Speech Recognition (ASR) in their design [45] [49]. However, though VUIs are frequently employed in close domains (e.g., smart phone) there are still important challenges to overcome [3].…”

Section: Smart Home and Aalmentioning

confidence: 99%

On Distant Speech Recognition for Home Automation

2015

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Abstract. In the framework of Ambient Assisted Living, home automation may be a solution for helping elderly people living alone at home. This study is part of the Sweet-Home project which aims at developing a new home automation system based on voice command to improve support and well-being of people in loss of autonomy. The goal of the study is vocal order recognition with a focus on two aspects: distance speech recognition and sentence spotting. Several ASR techniques were evaluated on a realistic corpus acquired in a 4-room flat equipped with microphones set in the ceiling. This distant speech French corpus was recorded with 21 speakers who acted scenarios of activities of daily living. Techniques acting at the decoding stage, such as our novel approach called Driven Decoding Algorithm (DDA), gave better speech recognition results than the baseline and other approaches. This solution which uses the two best SNR channels and a priori knowledge (voice commands and distress sentences) has demonstrated an increase in recognition rate without introducing false alarms. Generally speaking, a short overview allows then to outline the research challenges that speech technologies must take up for Ambient Assisted Living and Augmentative and Alternative Communication, and the current reseach avenues in this domain.

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“…The last corpus is the Cirdo-set corpus [3]. This corpus was recorded in the Living Lab of the LIG laboratory by 13 young adults (32min 01s) and 4 elderly people (age: 61-83 years, 28min 54s) which played 4 scenarios relative to fall, one to blocked hip and two True Negative (TN) scenarios.…”

Section: Corpus Acquisition and Analysis Systemmentioning

confidence: 99%