NDLI: Multilingual deep neural network based acoustic modeling for rapid language adaptation

Content Provider	IEEE Xplore Digital Library
Author	Ngoc Thang Vu Imseng, D. Povey, D. Motlicek, P. Schultz, T. Bourlard, H.
Copyright Year	2014
Description	Author affiliation: Idiap Res. Inst., Martigny, Switzerland (Imseng, D.; Motlicek, P.; Bourlard, H.) \|\| Johns Hopkins Univ., Baltimore, MD, USA (Povey, D.) \|\| Karlsruhe Inst. of Technol., Karlsruhe, Germany (Ngoc Thang Vu; Schultz, T.)
Abstract	This paper presents a study on multilingual deep neural network (DNN) based acoustic modeling and its application to new languages. We investigate the effect of phone merging on multilingual DNN in context of rapid language adaptation. Moreover, the combination of multilingual DNNs with Kullback-Leibler divergence based acoustic modeling (KL-HMM) is explored. Using ten different languages from the Globalphone database, our studies reveal that crosslingual acoustic model transfer through multilingual DNNs is superior to unsupervised RBM pre-training and greedy layer-wise supervised training. We also found that KL-HMM based decoding consistently outperforms conventional hybrid decoding, especially in low-resource scenarios. Furthermore, the experiments indicate that multilingual DNN training equally benefits from simple phoneset concatenation and manually derived universal phonesets.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	7639
Ending Page	7643
File Size	77208
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6855086
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	Hidden Markov models Training Acoustics Speech Decoding Training data Neural networks KL-HMM Multilingual DNN phone merging rapid language adaptation
Content Type	Text
Resource Type	Article

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