Integrating Articulatory Features using Kullback-Leibler Divergence based Acoustic Model for Phoneme Recognition
| Type of publication: | Idiap-RR |
| Citation: | Rasipuram_Idiap-RR-02-2011 |
| Number: | Idiap-RR-02-2011 |
| Year: | 2011 |
| Month: | 2 |
| Institution: | Idiap |
| 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 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 modelling AF probabilities and phoneme probabilities as features. This shows the efficacy and flexibility of the proposed approach. |
| Keywords: | articulatory features, Automatic Speech Recognition, Kullback-Leibler divergence based hidden Markov model, multilayer perceptron, phonemes, posterior probabilities |
| Projects |
Idiap |
| Authors | |
| Crossref by |
Rasipuram_ICASSP_2011 |
| Added by: | [ADM] |
| Total mark: | 0 |
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