%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Imseng_ASRU_2011,
                      author = {Imseng, David and Rasipuram, Ramya and Magimai-Doss, Mathew},
                    projects = {Idiap, IM2},
                       month = dec,
                       title = {Fast and flexible Kullback-Leibler divergence based acoustic modeling for non-native speech recognition},
                   booktitle = {Proceedings of the IEEE workshop on Automatic Speech Recognition and Understanding},
                        year = {2011},
                       pages = {348-353},
                    location = {Hawaii, USA},
                    crossref = {Imseng_Idiap-RR-01-2012},
                    abstract = {One of the main challenge in non-native speech recognition is how to handle acoustic variability present in multiaccented non-native speech with limited amount of training data. In this paper, we investigate an approach that addresses this challenge by using Kullback-Leibler divergence based hidden Markov models (KL-HMM). More precisely, the acoustic variability in the multi-accented speech is handled by using multilingual phoneme posterior probabilities, estimated by a multilayer perceptron trained on auxiliary data, as input feature for the KL-HMM system. With limited training data, we then build better acoustic models by exploiting the advantage that the KL-HMM system has fewer number of parameters. On HIWIRE corpus, the proposed approach yields a performance of 1.9\% word error rate (WER) with 149 minutes of training data and a performance of 5.5\% WER with 2 minutes of training data.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2011/Imseng_ASRU_2011.pdf}
}



crossreferenced publications: 
@TECHREPORT{Imseng_Idiap-RR-01-2012,
                      author = {Imseng, David and Rasipuram, Ramya and Magimai-Doss, Mathew},
                    keywords = {Hidden Markov Model, Kullback-Leibler divergence, multilayer perceptron, Posterior features},
                    projects = {Idiap, IM2},
                       month = {1},
                       title = {Fast and flexible Kullback-Leibler divergence based acoustic modeling for non-native speech recognition},
                        type = {Idiap-RR},
                      number = {Idiap-RR-01-2012},
                        year = {2012},
                 institution = {Idiap},
                    abstract = {One of the main challenge in non-native speech recognition is how to handle acoustic variability present in multiaccented non-native speech with limited amount of training data. In this paper, we investigate an approach that addresses this challenge by using Kullback-Leibler divergence based hidden Markov models (KL-HMM). More precisely, the acoustic variability in the multi-accented speech is handled by using multilingual phoneme posterior probabilities, estimated by a multilayer perceptron trained on auxiliary data, as input feature for the KL-HMM system. With limited training data, we then build better acoustic models by exploiting the advantage that the KL-HMM system has fewer number of parameters. On HIWIRE corpus, the proposed approach yields a performance of 1.9\% word error rate (WER) with 149 minutes of training data and a performance of 5.5\% WER with 2 minutes of training data.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2011/Imseng_Idiap-RR-01-2012.pdf}
}