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 [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.
Keywords:
Projects Idiap
Authors Rasipuram, Ramya
Magimai.-Doss, Mathew
Added by: [UNK]
Total mark: 0
Attachments
  • Rasipuram_ICASSP_2011.pdf
Notes