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Multilingual deep neural network based acoustic modeling for rapid language adaptation
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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
doi:10.1109/icassp.2014.6855086
dblp:conf/icassp/VuIPMSB14
fatcat:pxh4jbwbbjetplhdtcgr5hrb6i