Interpretation of Multiparty Meetings the AMI and Amida Projects

By S. Renals, Thomas Hain. 2008

28 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Dialogue & Conversational AI, 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].”
VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “…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 al., 2013), and…”
The Pytorch-kaldi Speech Recognition Toolkit (2018, background): “A number of speech-related projects such as AMI [4], DICIT [5], DIRHA [6] and speech recognition challenges such as CHiME [7], Babel, and Aspire, have remarkably fostered the progress in ASR.”
The third 'CHiME' speech separation and recognition challenge: Analysis and outcomes (2017, background): “, meeting rooms (Renals et al., 2008; RWCP, 2001) and lecture halls (Mostefa et al.”
End-to-End Neural Speaker Diarization with Permutation-Free Objectives (2019, background): “It also helps automatic speech recognition performance in multi-speaker conversation scenarios in meetings (ICSI [3,4], AMI [5,6]) and home environments (CHiME-5 [6–10]).”
Concept-Based Video Retrieval (2009, background): “Notable examples are the ETISEO [167] and PETS [60] initiatives, which focus on surveillance video, the AMI project [192], which emphasizes the analysis of meeting videos; VideoCLEF [122], which studies retrieval tasks related to multi-lingual video content; and, the US Government Video Analysis and”
An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, unknown): “, 1994), meetings (Renals et al., 2008), conversations (Harper, 2015), dialogues (Stupakov et al.”
Batch-normalized joint training for DNN-based distant speech recognition (2016, background): “Valuable examples include the AMI/AMIDA projects [3], who were focused on automatic meeting transcription, DICIT [4] which investigated voice-enabled TVs and, more recently, DIRHA which addressed speech-based domestic control.”
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].”
A French Corpus for Distant-Microphone Speech Processing in Real Homes (2016, unknown): “…and application scenarios such as voice command for cars [7–9] and in public spaces [10], automatic transcription of lectures [11], meetings [12–14], dialogues [15,16] and other public gatherings [17,18], and automatic transcription of noisy or overlapped speech in broadcast media [19,20].”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “Aurora-321 car ∼20 4 1 static no AMI [294]22 meeting 100 16 3 static yes DICIT [295]23 TV order 6 16 1 moving no COSINE [296]24 discuss.”