Title :
A subsequence-histogram method for generic vocabulary recognition over deletion channels
Author_Institution :
Hewlett-Packard Labs., Palo Alto, CA, USA
fDate :
June 28 2009-July 3 2009
Abstract :
We consider the problem of recognizing a vocabulary-a collection of words (sequences) over a finite alphabet-from a potential subsequence of one of its words. We assume the given subsequence is received through a deletion channel as a result of transmission of a random word from one of the two generic underlying vocabularies. An exact maximum a posterior (MAP) solution for this problem counts the number of ways a given subsequence can be derived from particular subsets of candidate vocabularies, requiring exponential time or space.
Keywords :
maximum likelihood estimation; speech recognition; deletion channels; maximum a posterior solution; subsequence-histogram method; vocabulary recognition; Approximation algorithms; Data mining; Databases; Histograms; Laboratories; Maximum likelihood decoding; Maximum likelihood detection; Natural languages; Polynomials; Vocabulary; Classification; histogram; recognition; search; storage; subsequence;
Conference_Titel :
Information Theory, 2009. ISIT 2009. IEEE International Symposium on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-4312-3
Electronic_ISBN :
978-1-4244-4313-0
DOI :
10.1109/ISIT.2009.5206011
