NDLI: Acoustic modeling for hindi speech recognition in low-resource settings

Content Provider	IEEE Xplore Digital Library
Author	Dey, A. Weibin Zhang Fung, P.
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong, China (Dey, A.; Weibin Zhang; Fung, P.)
Abstract	We propose an approach for acoustic modeling of Hindi speech by borrowing from English data, for the purpose of Hindi LVCSR. Hindi, like many Indian languages, has a significant speaker base but there have not been a lot of resources to obtain large amounts of transcribed Hindi data for LVCSR. We compare a baseline Gaussian model-sharing approach with DNN training. A widely used data-borrowing method with DNN is to firstly train a DNN with English, for which a large amount of training data is available; then the whole DNN, except the last layer, is fine-tuned by using the target Hindi data. We propose to do phonetic mapping between Hindi and English in the first stage, training Hindi acoustic models by sharing data between Hindi-English phone pairs in the second stage, and finally fine-tuning the acoustic model by using the Hindi data. We evaluate and compare these approaches with experiments using 1 hour of transcribed Hindi data and 15 hours of Wall Street Journal English data. Experiments show that the proposed method significantly outperforms conventional baseline models in a low-resource setting for phone recognition tasks.
Starting Page	891
Ending Page	894
File Size	219426
Page Count	4
File Format	PDF
ISBN	9781479939022
e-ISBN	9781479939039
DOI	10.1109/ICALIP.2014.7009923
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training low resource data borrowing Hindi LVSCR Hidden Markov models Speech recognition Speech Feature extraction Acoustics Data models phone mapping
Content Type	Text
Resource Type	Article

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