DocumentCode :
3426238
Title :
Towards link characterization from content
Author :
Grothendieck, John ; Gorin, Allen
Author_Institution :
Rutgers Univ., Newark, NJ
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
4849
Lastpage :
4852
Abstract :
In processing large volumes of speech and language data, we are often interested in the distribution of languages, speakers, topics, etc. For large data sets, these distributions are typically estimated at a given point in time using pattern classification technology. Such estimates can be highly biased, especially for rare classes. While these biases have been addressed in some applications, they have thus far been ignored in the speech and language literature. This neglect causes significant error for low-frequency classes. Correcting this biased distribution involves exploiting uncertain knowledge of the classifier error patterns. The Metropolis-Hastings algorithm allows us to construct a Bayes estimator for the true class proportions. We experimentally evaluate this algorithm for a speaker recognition task. In this experiment, the Bayes estimator reduces maximum RMSE by a factor of five. Performance is furthermore more consistent, with range of RMSE reduced by a factor of 4.
Keywords :
Bayes methods; speaker recognition; Bayes estimator; Metropolis-Hastings algorithm; language processing; link characterization; pattern classification technology; speaker recognition task; speech processing; Bayesian methods; Error analysis; Humans; Internet; Natural languages; Speech analysis; Speech processing; Statistical distributions; Telecommunication traffic; Uncertainty; Monte Carlo methods; knowledge acquisition; speech processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4518743
Filename :
4518743
Link To Document :
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