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Abstract
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| We present a novel approach for language identification based on a text categorization technique, namely an n-gram frequency ranking. We use a Parallel phone recognizer, the same as in PPRLM, but instead of the language model, we create a ranking with the most frequent n-grams, keeping only a fraction of them. Then we compute the distance between the input sentence ranking and each language ranking, based on the difference in relative positions for each n-gram. The objective of this ranking is to be able to model reliably a longer span than PPRLM, namely 5-gram instead of trigram, because this ranking will need less training data for a reliable estimation. We demonstrate that this approach overcomes PPRLM (6% relative improvement) due to the inclusion of 4- gram and 5-gram in the classifier. We present two alternatives: ranking with absolute values for the number of occurrences and ranking with discriminative values (11% relative improvement). | |
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International
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Si |
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Congress
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8th Annual Conference of the Internacional Speech Communication Association (Interspeech 2007) |
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960 |
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Place
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Antwerp, Belgium |
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Reviewers
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Si |
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ISBN/ISSN
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ISSN 1990-9772 |
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Start Date
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27/08/2007 |
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End Date
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31/08/2007 |
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From page
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