Integrating articulatory features using Kullback-Leibler divergence based acoustic model for phoneme recognition

Abstract

In this paper, we propose a novel framework to integrate articulatory features (AFs) into HMM- based ASR system. This is achieved by using posterior probabilities of different AFs (estimated by multilayer perceptrons) directly as observation features in Kullback-Leibler divergence based HMM (KL-HMM) system. On the TIMIT phoneme recognition task, the… (More)
DOI: 10.1109/ICASSP.2011.5947527

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Cite this paper

@article{Rasipuram2011IntegratingAF, title={Integrating articulatory features using Kullback-Leibler divergence based acoustic model for phoneme recognition}, author={Ramya Rasipuram and Mathew Magimai-Doss}, journal={2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2011}, pages={5192-5195} }