NDLI: CRIM and LIUM approaches for multi-genre broadcast media transcription

Content Provider	IEEE Xplore Digital Library
Author	Gupta, Vishwa Deleglise, Paul Boulianne, Gilles Esteve, Yannick Meignier, Sylvain Rousseau, Anthony
Copyright Year	2015
Description	Author affiliation: Centre de recherche informatique de Montréal (CRIM) (Gupta, Vishwa; Boulianne, Gilles) \|\| LIUM - University of Le Mans, France (Deleglise, Paul; Esteve, Yannick; Meignier, Sylvain; Rousseau, Anthony)
Abstract	The Multi-Genre Broadcast Challenge at ASRU 2015 is a controlled evaluation of speech recognition, speaker diarization, and lightly supervised alignment using BBC TV recordings. CRIM and LIUM teams participated in the speech recognition part of the challenge with a joint submission. This paper presents the CRIM and LIUM's contributions. Each team made different choices to develop its ASR system. By the way, it was expected to compare and to evaluate different approaches to diarization and acoustic modeling, and to get complementary ASR systems for effective merging. CRIM's main contributions are the use of a training scenario similar to multi-lingual training to estimate the deep neural net (DNN) acoustic models with most of the data, the use of a pruned trigram model for search, in addition to the use of a genre-dependent quadgram language model for rescoring the lattice from the search. For LIUM, the focus was on fast decoding with high accuracy. The final word error rates (WER) after merging show that it is possible to get reasonable WER with automatically aligned files. The final global WER of 25.1% corresponds to a WER reduction of about 20% absolute in comparison to the ASR baseline system provided by the organizers.
Starting Page	681
Ending Page	686
File Size	137391
Page Count	6
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404862
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Acoustics Speech Training data Data models Hidden Markov models Speech recognition multi-genre broadcast transcription Deep Neural Networks DNN change point detection automatic transcription
Content Type	Text
Resource Type	Article

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