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Multilingual training of deep neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages7319-7323
Number of pages5
ISBN (Print)978-1-4799-0356-6
DOIs
Publication statusPublished - 2013

Abstract

We investigate multilingual modeling in the context of a deep neural network (DNN) - hidden Markov model (HMM) hybrid, where the DNN outputs are used as the HMM state likelihoods. By viewing neural networks as a cascade of feature extractors followed by a logistic regression classifier, we hypothesise that the hidden layers, which act as feature extractors, will be transferable between languages. As a corollary, we propose that training the hidden layers on multiple languages makes them more suitable for such cross-lingual transfer. We experimentally confirm these hypotheses on the GlobalPhone corpus using seven languages from three different language families: Germanic, Romance, and Slavic. The experiments demonstrate substantial improvements over a monolingual DNN-HMM hybrid baseline, and hint at avenues of further exploration.

    Research areas

  • feature extraction, hidden Markov models, linguistics, natural language processing, neural nets, pattern classification, regression analysis, speech recognition, Germanic language, GlobalPhone corpus, HMM state likelihoods, Romance language, Slavic language, cross-lingual transfer, deep neural network, feature extractors, hidden Markov model, hidden layers, hybrid DNN-HMM, logistic regression classifier, multilingual modeling, multilingual training, Acoustics, Feature extraction, Hidden Markov models, Neural networks, Speech, Speech recognition, Training, deep learning, neural networks

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