An analysis of sparseness and regularization in exemplar-based methods for speech classification

Dimitri Kanevsky, Tara N. Sainath, Bhuvana Ramabhadran, David Nahamoo

The use of exemplar-based techniques for both speech classification and recognition tasks has become increasingly popular in recent years. However, the notion of why sparseness is important for exemplar-based speech processing has been relatively unexplored. In addition, little analysis has been done in speech processing on the appropriateness of different types of sparsity regularization constraints. The goal of this paper is to answer the above two questions, both through mathematically analyzing different sparseness methods and also comparing these approaches for phonetic classification in TIMIT.

doi: 10.21437/Interspeech.2010-303

Cite as: Kanevsky, D., Sainath, T.N., Ramabhadran, B., Nahamoo, D. (2010) An analysis of sparseness and regularization in exemplar-based methods for speech classification. Proc. Interspeech 2010, 2842-2845, doi: 10.21437/Interspeech.2010-303

@inproceedings{kanevsky10_interspeech,
  author={Dimitri Kanevsky and Tara N. Sainath and Bhuvana Ramabhadran and David Nahamoo},
  title={{An analysis of sparseness and regularization in exemplar-based methods for speech classification}},
  year=2010,
  booktitle={Proc. Interspeech 2010},
  pages={2842--2845},
  doi={10.21437/Interspeech.2010-303},
  issn={2308-457X}
}