The Sweet-Home speech and multimodal corpus for home automation interaction
By Michel Vacher, Benjamin Lecouteux, Pedro Chahuara, François Portet, B. Meillon, Nicolas Bonnefond. 2014
76 citations recorded by the source corpus.
Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes and Home Automation. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The S WEET-H OME multimodal corpus is a dataset recorded in realistic conditions in D OMUS, a fully equipped Smart Home with microphones and home automation sensors, in which participants performed Activities of Daily living (ADL). This corpus is made of a multimodal subset, a French home automation speech subset recorded in Distant Speech conditions, and two interaction subsets, the first one being recorded by 16 persons without disabilities and the second one by 6 seniors and 5 visually impaired people. This corpus was used in studies related to ADL recognition, context aware interaction and distant speech recognition applied to home automation controled through voice.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Evaluation & Benchmarks, Speech & Audio, Signal Processing
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Citations in the corpus
- CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “The CHiME-1 [15] and CHiME-2 [16] challenges and corpora have contributed to popularizing research on this topic, together with the DICIT [17], Sweet-Home [18], and DIRHA [19] corpora.”
- VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “The DIRHA-English corpus (Ravanelli et al., 2015) and the Sweet-Home corpus (Vacher et al., 2014) relax this constraint, but are not publicly available 2 .”
- The SINS Database for Detection of Daily Activities in a Home Environment Using an Acoustic Sensor Network (2017, methodology): “The task considered here is to detect an activity being performed, similar to the work in [3, 4, 5, 6].”
- The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines (2018, unknown): “The CHiME-1 [14] and CHiME-2 [15] challenges and corpora have contributed to popularizing research on this topic, together with the DICIT [16], Sweet-Home [17], and DIRHA [18] corpora.”
- A French Corpus for Distant-Microphone Speech Processing in Real Homes (2016, unknown): “The DIRHA-English corpus [26] and the Sweet-Home corpus [27] relax this constraint, but are not publicly available(1).”