logo Idiap Research Institute        
 [BibTeX] [Marc21]
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.
Projects Idiap
Authors Rasipuram, Ramya
Magimai.-Doss, Mathew
Added by: [UNK]
Total mark: 0
  • Rasipuram_ICASSP_2011.pdf