%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Rasipuram_ICASSP_2011,
                      author = {Rasipuram, Ramya and Magimai-Doss, Mathew},
                    projects = {Idiap},
                       title = {Integrating articulatory features using Kullback-Leibler divergence based acoustic model for phoneme recognition},
                   booktitle = {Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP},
                        year = {2011},
                       pages = {5192 - 5195},
                         doi = {10.1109/ICASSP.2011.5947527},
                    crossref = {Rasipuram_Idiap-RR-02-2011},
                    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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2011/Rasipuram_ICASSP_2011.pdf}
}



crossreferenced publications: 
@TECHREPORT{Rasipuram_Idiap-RR-02-2011,
                      author = {Rasipuram, Ramya and Magimai-Doss, Mathew},
                    keywords = {articulatory features, Automatic Speech Recognition, Kullback-Leibler divergence based hidden Markov model, multilayer perceptron, phonemes, posterior probabilities},
                    projects = {Idiap},
                       month = {2},
                       title = {Integrating Articulatory Features using Kullback-Leibler Divergence based Acoustic Model for Phoneme Recognition},
                        type = {Idiap-RR},
                      number = {Idiap-RR-02-2011},
                        year = {2011},
                 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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2010/Rasipuram_Idiap-RR-02-2011.pdf}
}