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Neural net word representations for phrase-break prediction without a part of speech tagger

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    Rights statement: © Watts, O., Gangireddy, S., Yamagishi, J., King, S., Renals, S., Stan, A., & Giurgiu, M. (2014). Neural net word representations for phrase-break prediction without a part of speech tagger. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. (pp. 2599-2603). [6854070] Institute of Electrical and Electronics Engineers Inc.. 10.1109/ICASSP.2014.6854070

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Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2599-2603
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 4 May 2014
EventICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Florence, Italy, Florence, United Kingdom
Duration: 4 May 20149 May 2014

Conference

ConferenceICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryUnited Kingdom
CityFlorence
Period4/05/149/05/14

Abstract

The use of shared projection neural nets of the sort used in language modelling is proposed as a way of sharing parameters between multiple text-to-speech system components. We experiment with pretraining the weights of such a shared projection on an auxiliary language modelling task and then apply the resulting word representations to the task of phrase-break prediction. Doing so allows us to build phrase-break predictors that rival conventional systems without any reliance on conventional knowledge-based resources such as part of speech taggers.

    Research areas

  • multitask learning, neural net language modelling, Speech synthesis, TTS, unsupervised learning

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