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 [BibTeX] [Marc21]
Segment-level training of ANNs based on acoustic confidence measures for hybrid HMM/ANN Speech Recognition
Type of publication: Conference paper
Citation: Dubagunta_ICASSP_2019
Publication status: Accepted
Booktitle: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Year: 2019
Abstract: We show that confidence measures estimated from local posterior probabilities can serve as objective functions for training ANNs in hybrid HMM based speech recognition systems. This leads to a segment-level training paradigm that overcomes the limitation of frame-level updates ignoring the sequence structure in speech. We propose measures that train at the state and phone segment levels, while still decoding in the conventional framework. Experimental results on multiple corpora show that such trainings not only yield better systems in terms of performance, but also give additional improvements with sequence discriminative training. These techniques generalise across front-ends and model architectures, and efficiently handle the effect of segment duration variations on the ANN training.
Keywords: confidence measures, local posterior probability, segment-level training., speech recognition
Projects Idiap
Authors Dubagunta, S. Pavankumar
Magimai.-Doss, Mathew
Added by: [UNK]
Total mark: 0
  • Dubagunta_ICASSP_2019.pdf