Fuzzy clustering in HMM-based triphone classes of 2DLDA in Slovak LVCSR

@article{Conka2016FuzzyCI,
  title={Fuzzy clustering in HMM-based triphone classes of 2DLDA in Slovak LVCSR},
  author={David Conka and Peter Viszlay and Jozef Juh{\'a}r},
  journal={2016 International Conference on Systems, Signals and Image Processing (IWSSIP)},
  year={2016},
  pages={1-4}
}
This paper presents a new approach to improve the standard class definition in two-dimensional linear discriminant analysis (2DLDA). It is known that an HMM-based triphone class contains data collected from many speakers with different speech variability. Thus, there exist many clusters in each class, whose composition has an influence to 2DLDA. We propose to employ the fuzzy C-means clustering to identify the clusters in each class and treat them as new classes. This work follows our past… CONTINUE READING

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