The CHIL audiovisual corpus for lecture and meeting analysis inside smart rooms

By Djamel Mostefa, Nicolas Moreau, Khalid Choukri, Gerasimos Potamianos, Stephen M. Chu, Ambrish Tyagi, J. Casas, Jordi Turmo, L. Cristoforetti, F. Tobia and 7 more. 2007

115 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Language and Linguistics, Sociology and Political Science, Evaluation & Benchmarks, Speech & Audio

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Citations in the corpus

CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “Early attempts in this direction can be traced back to the ICSI [26], CHIL [27], and AMI [28] meeting corpora, the LLSEC [29] and COSINE [30] face-to-face interaction corpora, and the Shefﬁeld Wargames corpus [31].”
The third 'CHiME' speech separation and recognition challenge: Analysis and outcomes (2017, unknown): “These have typically focussed on genuine distant microphone speech recognition scenarios but ones that feature relatively benign environments where high SNRs can be expected, e.g., meeting rooms (Renals et al., 2008; RWCP, 2001) and lecture halls (Mostefa et al., 2007).”
VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “…et al., 2004) and in public spaces (Barker et al., 2015), automatic transcription of lectures (Lamel et al., 1994), meetings (Janin et al., 2003; Mostefa et al., 2007; Renals et al., 2008), dialogs (LLSEC, 1996; Stupakov et al., 2011) and other public gatherings (Lincoln et al., 2005; Fox et…”
The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines (2015, unknown): “Most of these challenges, as exempliﬁed by RWCP-SP, CHIL, AMI, PASCAL SSC2, and REVERB, have been designed for lecture, meeting, or conversation scenarios [1–4] involving reverberated speech recorded by distant microphones in essentially quiet environments.”
AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario (2021, background): “[9] D. Mostefa, N. Moreau, K. Choukri, G. Potamianos, S. M. Chu, A. Tyagi, J. R. Casas, J. Turmo, L. Cristoforetti, F. Tobia et al., “The CHIL audiovisual corpus for lecture and meeting analysis inside smart rooms,” Language resources and evaluation, vol. 41, no. 3, pp. 389–407, 2007.”
Evaluation of video activity localizations integrating quality and quantity measurements (2014, background): “Consequently, we see an early drop in performance varying any one of the thresholds, t sr = 1 or t sp = 1 .”
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines (2018, unknown): “Early attempts in this direction can be traced back to the ICSI [24], CHIL [25], and AMI [26] meeting corpora, the LLSEC [27] and COSINE [28] face-to-face interaction corpora, and the Shefﬁeld Wargames corpus [29].”
Realistic Multi-Microphone Data Simulation for Distant Speech Recognition (2016, methodology): “Other simulated corpora have been released under the CHIL project [27] and, more recently, under the EU DIRHA project [28, 29, 30, 31, 32].”