Unsupervised adaptation without estimated transriptions

Hyeopwoo Lee, Dongsuk Yook

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

    Abstract

    To estimate the unknown distortion parameters from input test signals, estimated transcriptions are typically used for unsupervised adaptation. In a low signal to noise ratio (SNR) condition, the transcription estimated by a decoding procedure can be error prone because of the high mismatch between the acoustic models and the input signal. As a result, it can cause performance degradation of the adapted systems. To account for this problem, we propose an unsupervised adaptation method that can adapt the acoustic models without the estimated transcription. Instead, Gaussian mixture models (GMM) and pseudo phoneme models (PPM) are used. Using these models the unknown distortion parameters are estimated based on the vector Taylor series (VTS) model adaptation scheme. On the Aurora2 task, we obtained relative reduction of 5.4% in word error rate (WER).

    Original languageEnglish
    Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
    Pages7918-7921
    Number of pages4
    DOIs
    Publication statusPublished - 2013 Oct 18
    Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
    Duration: 2013 May 262013 May 31

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ISSN (Print)1520-6149

    Other

    Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
    Country/TerritoryCanada
    CityVancouver, BC
    Period13/5/2613/5/31

    Keywords

    • Unsupervised adaptation
    • robust speech recognition
    • vector Taylor series

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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