Integrating articulatory features using Kullback-Leibler divergence based acoustic model for phoneme recognition
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 proposed framework yields a phoneme recognition accuracy of 72.4% which is comparable to KL-HMM system using posterior probabilities of phonemes as features (72.7%). Furthermore, a best performance of 73.5% phoneme recognition accuracy is achieved by jointly modeling AF probabilities and phoneme probabilities as features. This shows the efficacy and flexibility of the proposed approach.
- Related documents: http://publications.idiap.ch/index.php/publications/showcite/Rasipuram_Idiap-RR-02-2011
Record created on 2013-12-19, modified on 2016-08-09