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- W15693373 abstract "Finding measures (or features) invariant to inevitable variations caused by non-linguistical factors (transformations) is a fundamental yet important problem in speech recognition. Recently, Minematsu [1, 2] proved that Bhattacharyya distance (BD) between two distributions is invariant to invertible transforms on feature space, and develop an invariant structural representation of speech based on it. There is a question: which kind of measures can be invariant? In this paper, we prove that f -divergence yields a generalized family of invariant measures, and show that all the invariant measures have to be written in the forms of f -divergence. Many famous distances and divergences in information and statistics, such as Bhattacharyya distance (BD), KL-divergence, Hellinger distance, can be written into forms of f -divergence. As an application, we carried out experiments on recognizing the utterances of connected Japanese vowels. The experimental results show that BD and KL have the best performance among the measures compared." @default.
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- W15693373 date "2008-09-22" @default.
- W15693373 modified "2023-10-01" @default.
- W15693373 title "f-divergence is a generalized invariant measure between distributions" @default.
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- W15693373 doi "https://doi.org/10.21437/interspeech.2008-393" @default.
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