Paper Info
Title
Automatic allocation of training data for rapid prototyping of speech understanding based on multiple model combination
Abstract
The optimal choice of speech understanding method depends on the amount of training data available in rapid prototyping. A statistical method is ultimately chosen, but it is not clear at which point in the increase in training data a statistical method become effective. Our framework combines multiple automatic speech recognition (ASR) and language understanding (LU) modules to provide a set of speech understanding results and selects the best result among them. The issue is how to allocate training data to statistical modules and the selection module in order to avoid overfitting in training and obtain better performance. This paper presents an automatic training data allocation method that is based on the change in the coefficients of the logistic regression functions used in the selection module. Experimental evaluation showed that our allocation method outperformed baseline methods that use a single ASR module and a single LU module at every point while training data increase.
Year
Venue
Keywords
2010
COLING (Posters)
allocation method,single asr module,speech understanding method,rapid prototyping,selection module,training data,single lu module,automatic allocation,multiple model combination,training data increase,statistical method,automatic training data allocation
Field
DocType
Volume
Rapid prototyping,Training set,Computer science,Speech recognition,Artificial intelligence,Overfitting,Logistic regression,Machine learning,Language understanding
Conference
C10-2
Citations 
PageRank 
References 
2
0.37
12
Authors
6
Name
Order
Citations
PageRank
Kazunori Komatani179087.95
Masaki Katsumaru282.23
Mikio Nakano348861.92
Kotaro Funakoshi422231.49
Tetsuya Ogata51158135.73
Hiroshi G. Okuno62092233.19