A Proposal for a Kernel-based Algorithm for Large Vocabulary Continuous Speech Recognition
| Type of publication: | Book chapter |
| Citation: | Keshet_WILEY-3_2009 |
| 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. |
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| Added by: | [UNK] |
| Total mark: | 0 |
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