Hidden neural networks: application to speech recognition

Søren Kamaric Riis

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    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks (HNNs) with much fewer parameters than conventional HMMs and other hybrids can obtain comparable performance, and for the broad class task it is illustrated how the HNN can be applied as a purely transition based system, where acoustic context dependent transition probabilities are estimated by neural networks
    Original languageEnglish
    Title of host publicationAcoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
    Publication date1998
    ISBN (Print)0-7803-4428-6
    Publication statusPublished - 1998
    Event1998 IEEE International Conference on Acoustics, Speech and Signal Processing - Seattle, United States
    Duration: 12 May 199815 May 1998
    Conference number: 23


    Conference1998 IEEE International Conference on Acoustics, Speech and Signal Processing
    Country/TerritoryUnited States

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