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@INCOLLECTION{Keshet_WILEY-3_2009,
                      author = {Keshet, Joseph},
                      editor = {Keshet, Joseph and Bengio, Samy},
                    projects = {Idiap},
                       title = {A Proposal for a Kernel-based Algorithm for Large Vocabulary Continuous Speech Recognition},
                   booktitle = {Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods},
                        year = {2009},
                   publisher = {John Wiley and Sons},
                    abstract = {We present a proposal of a kernel-based model for large vocabulary continuous speech recognizer. The continuous speech recognition is described as a problem of finding the best phoneme sequence and its best time span, where the phonemes are generated from all permissible word sequences. A non-probabilistic score is assigned to every phoneme sequence and time span sequence, according to a kernel-based acoustic model and a kernel-based language model. The acoustic model is described in terms of segments, where each segment corresponds to a whole phoneme, and it generalizes Segmental Models for the non-probabilistic setup. The language model  is based on discriminative language model  recently proposed by Roark et al. (2007). We devise a loss function  based on the word error rate and present a large margin training  procedure for the kernel models, which aims at minimizing this loss function. Finally, we discuss the practical issues of the implementation of kernel-based continuous speech recognition  model by presenting an efficient iterative algorithm and considering the decoding process. We conclude the chapter by a brief discussion on the model limitations and future work. This chapter does not introduce any experimental results.}
}