Paper Info
Title
Hill-Climbing Feature Selection For Multi-Stream Asr
Abstract
We performed automated feature selection for multi-stream (i.e., ensemble) automatic speech recognition, using a hill-climbing (HC) algorithm that changes one feature at a time if the change improves a performance score. For both clean and noisy data sets (using the OGI Numbers corpus), HC usually improved performance on held out data compared to the initial system it started with, even for noise types that were not seen during the HC process. Overall, we found that using Opitz's scoring formula, which blends single-classifier word recognition accuracy and ensemble diversity, worked better than ensemble accuracy as a performance score for guiding HC in cases of extreme mismatch between the SNR of training and test sets.Our noisy version of the Numbers corpus, our multi-layer-perceptron-based Numbers ASR system, and our HC scripts are available online.
Year
Venue
Keywords
2009
INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5
speech recognition, feature selection, ensemble
Field
DocType
Citations 
Hill climbing,Noisy data,Pattern recognition,Ensemble diversity,Feature selection,Computer science,Word recognition,Speech recognition,Feature (machine learning),Artificial intelligence,Scripting language
Conference
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
David Gelbart113417.54
Nelson Morgan23048533.52
Alexey Tsymbal385887.84