Integrating articulatory features using Kullback-Leibler divergence based acoustic model for phoneme recognition
| Type of publication: | Conference paper |
| Citation: | Rasipuram_ICASSP_2011 |
| Publication status: | Published |
| Booktitle: | Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP |
| Year: | 2011 |
| Pages: | 5192 - 5195 |
| Crossref: | Rasipuram_Idiap-RR-02-2011: |
| DOI: | 10.1109/ICASSP.2011.5947527 |
| 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 modeling AF probabilities and phoneme probabilities as features. This shows the efficacy and flexibility of the proposed approach. |
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| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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