NDLI: Improving data selection for low-resource STT and KWS

Content Provider	IEEE Xplore Digital Library
Author	Fraga-Silva, Thiago Laurent, Antoine Gauvain, Jean-Luc Lamel, Lori Le, Viet-Bac Messaoudi, Abdel
Copyright Year	2015
Description	Author affiliation: CNRS/LIMSI, Spoken Language Processing Group, 91405 Orsay Cedex, France (Gauvain, Jean-Luc; Lamel, Lori) \|\| Vocapia Research, 28 rue Jean Rostand, 91400 Orsay, France (Fraga-Silva, Thiago; Laurent, Antoine; Le, Viet-Bac; Messaoudi, Abdel)
Abstract	This paper extends recent research on training data selection for speech transcription and keyword spotting system development. Selection techniques were explored in the context of the IARPA-Babel Active Learning (AL) task for 6 languages. Different selection criteria were considered with the goal of improving over a system built using a pre-defined 3-hour training data set. Four variants of the entropy-based criterion were explored: words, triphones, phones as well as the use of HMM-states previously introduced in [4]. The influence of the number of HMM-states was assessed as well as whether automatic or manual reference transcripts were used. The combination of selection criteria was investigated, and a novel multi-stage selection method proposed. This method was also assessed using larger data sets than were permitted in the Babel AL task. Results are reported for the 6 languages. The multi-stage selection was also applied to the surprise language (Swahili) in the NIST OpenKWS 2015 evaluation.
Starting Page	153
Ending Page	159
File Size	363526
Page Count	7
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404788
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	Speech Hidden Markov models Acoustics Entropy Training Decoding Training data keyword spotting data selection low-resource languages speech recognition
Content Type	Text
Resource Type	Article

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