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Abstract
In this paper we investigate techniques to combine hybrid HMM-DNN (hidden Markov model - deep neural network) and tandem HMM-GMM (hidden Markov model - Gaussian mixture model) acoustic models using: (1) model averaging, and (2) lattice combination with Minimum Bayes Risk decoding. We have performed experiments on the ''TED Talks" task following the protocol of the IWSLT-2012 evaluation. Our experimental results suggest that DNN-based and GMM-based acoustic models are complementary, with error rates being reduced by up to 8% relative when the DNN and GMM systems are combined at model-level in a multi-pass automatic speech recognition (ASR) system. Additionally, further gains were obtained by combining model-averaged lattices with the one obtained from baseline systems.
Original language | English |
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Title of host publication | Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 6744-6748 |
Number of pages | 5 |
ISBN (Print) | 978-1-4799-0356-6 |
DOIs | |
Publication status | Published - 2013 |
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