Sparse Modeling of Posterior Exemplars for Keyword Detection
| Type of publication: | Conference paper |
| Citation: | Ram_INTERSPEECH_2015 |
| Publication status: | Published |
| Booktitle: | Proceedings of Interspeech |
| Year: | 2015 |
| Month: | September |
| Pages: | 3690-3694 |
| Abstract: | Sparse representation has been shown to be a powerful modeling framework for classification and detection tasks. In this paper, we propose a new keyword detection algorithm based on sparse representation of the posterior exemplars. The posterior exemplars are phone conditional probabilities obtained from a deep neural network. This method relies on the concept that a keyword exemplar lies in a low-dimensional subspace which can be represented as a sparse linear combination of the training exemplars. The training exemplars are used to learn a dictionary for sparse representation of the keywords and background classes. Given this dictionary, the sparse representation of a test exemplar is used to detect the keywords. The experimental results demonstrate the potential of the proposed sparse modeling approach and it compares favorably with the state-of-the-art HMM-based framework on Numbers'95 database. |
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Idiap PHASER 200021-153507 |
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| Added by: | [UNK] |
| Total mark: | 0 |
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