Discriminative training of self-structuring hidden control neural models

Helge Bjarup Dissing Sørensen, Uwe Hartmann, Preben Hunnerup

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Abstract

This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus we developed a discriminative training algorithm for SHC models, where each SHC model for a specific speech pattern is trained with utterances of the pattern to be recognized and with other utterances. The discriminative training of SHC neural models has been tested on the TIDIGITS database
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
VolumeVolume 5
PublisherIEEE
Publication date1995
Pages3379-3382
ISBN (Print)07-80-32431-5
DOIs
Publication statusPublished - 1995
EventIEEE International Conference on Acoustics, Speech, and Signal Processing 1995 - Detroit, MI, United States
Duration: 9 May 199512 May 1995

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing 1995
CountryUnited States
CityDetroit, MI
Period09/05/199512/05/1995

Bibliographical note

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