The MGB challenge: Evaluating multi-genre broadcast media recognition

By Peter Bell, Mark John Francis Gales, Thomas Hain, J. Kilgour, P. Lanchantin, Xunying Liu, A. McParland, S. Renals, Oscar Saz-Torralba, Mirjam Wester and 1 more. 2015

138 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Language and Linguistics, Multimodal Learning, 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): “This can be attributed to advances in speech processing, audio enhancement, and machine learning, but also to the availability of real speech corpora recorded in cars [7, 8], quiet indoor environments [9, 10], noisy indoor and outdoor environments [11, 12], and challenging broadcast media [13,14].”
VoxCeleb: A Large-Scale Speaker Identification Dataset (2017, methodology): “Datasets obtained from multi-speaker environments include those from recorded meeting data [23, 24], or from audio broadcasts [25].”
A French Corpus for Distant-Microphone Speech Processing in Real Homes (2016, unknown): “A number of real corpora are now publicly available for environments 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 transcript”
An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, unknown): “…homes (Ravanelli et al., 2015), or tablets (Barker et al., 2015), and automatic transcription of lectures (Lamel et al., 1994), meetings (Renals et al., 2008), conversations (Harper, 2015), di-alogues (Stupakov et al., 2011), game sessions (Fox et al., 2013), or broadcast media (Bell et al., 2015).”
The MGB-5 Challenge: Recognition and Dialect Identification of Dialectal Arabic Speech (2019, background): “The first edition of the MGB challenge (MGB-1) [1] focused recognition, diarization, and alignment of BBC English TV output across four channels.”
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines (2018, unknown): “This can be attributed to advances in speech processing, audio enhancement, and machine learning, but also to the availability of real speech corpora recorded in cars [6, 7], quiet indoor environments [8, 9], noisy indoor and outdoor environments [10, 11], and challenging broadcast media [12,13].”
VoiceHome-2, an extended corpus for multichannel speech processing in real homes (2019, unknown): “…(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 automatic transcription of noisy or overlapped speech in broadcast media (Gravier et al., 2012; Bell et al., 2015).”
Voxceleb: Large-scale speaker verification in the wild (2020, methodology): “, 1986)y Clean speech 630 6300 MGB Challenge Dataset (Bell et al., 2015) Broadcast Data ** Unknown 1600 hs SITW (McLaren et al.”