NDLI: Narrow Adaptive Regularization of weights for grapheme-to-phoneme conversion

Content Provider	IEEE Xplore Digital Library
Author	Kubo, K. Sakti, S. Neubig, G. Toda, T. Nakamura, S.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Inf. Sci., Nara Inst. of Sci. & Technol. (NAIST), Ikoma, Japan (Kubo, K.; Sakti, S.; Neubig, G.; Toda, T.; Nakamura, S.)
Abstract	As the speech recognition field proceeds to open domain and multilingual tasks, the need for robust g2p conversion has been increasing. Towards this objective, we propose a new g2p conversion training method based on the Narrow Adaptive Regularization of Weights (NAROW) online learning algorithm. NAROW improves over its predecessor AROW by automatically adjusting hyperparameters to reduce mistake bounds, and ensuring that the learning rate is not updated when features for the input data have already been updated enough. The contribution of this paper is first to extend NAROW to structured learning, and show the inequality to bound the maximum number of errors in structured NAROW. In experiments, our proposed approach significantly improved over MIRA with consistent phoneme error rate reductions of 1.3-3.8% on a variety of dictionaries.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	2589
Ending Page	2593
File Size	115306
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854068
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Joints Training Error analysis Vectors Prediction algorithms Context Convex functions NAROW g2p conversion out-of-vocabulary word online discriminative training structured learning
Content Type	Text
Resource Type	Article

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