Paper Info
Title
Improving utterance verification using a smoothed naive Bayes model
Abstract
Utterance verification can be seen as a conventional pattern classification problem in which a feature vector is obtained for each hypothesized word in order to classify it as either correct or incorrect. It is unclear, however, which predictor (pattern) features and classification model should be used. Regarding the features, we have proposed a new feature, called word trellis stability (WTS), that can be profitably used in conjunction with more or less standard features such as acoustic stability. This is confirmed in this paper, where a smoothed naive Bayes classification model is proposed to adequately combine predictor features. On a series of experiments with this classification model and several features, we have found that the results provided by each feature alone are outperformed by certain combinations. In particular, the combination of the two above-mentioned features has been consistently found to give the most accurate result in two verification tasks.
Year
DOI
Venue
2003
10.1109/ICASSP.2003.1198850
ICASSP '03). 2003 IEEE International Conference
Keywords
Field
DocType
Bayes methods,feature extraction,prediction theory,signal classification,smoothing methods,speech recognition,acoustic stability,feature vector,pattern classification problem,predictor features,smoothed naive Bayes classification model,speech recognition verification,statistical language modelling,utterance verification,verification tasks,word trellis stability
Feature vector,Pattern recognition,Naive Bayes classifier,Computer science,Feature (computer vision),Utterance,Speech recognition,Feature extraction,Feature (machine learning),Signal classification,Artificial intelligence,Bayes classifier
Conference
Volume
ISSN
ISBN
1
1520-6149
0-7803-7663-3
Citations 
PageRank 
References 
8
0.91
7
Authors
3
Name
Order
Citations
PageRank
Alberto Sanchis1737.67
alfons juan257261.45
Enrique Vidal3109685.46