Paper Info
Title
Iterative Group Selection-Based Enhancement Of Time-Frequency Masks For Missing Data Recognition
Abstract
Missing data approaches have recently been applied to speech recognition tasks to increase noise robustness. The drawback of missing data techniques is the vulnerability of the recognizer to errors in the reliability mask. This work proposes a novel group selection algorithm to perform top-down refinement of initial bottom-up reliability mask estimates with the goal of removing these errors. A novel probabilistic decision process based on normalized likelihood distances is proposed and used to evaluate the quality of a reliability mask without any a priori noise knowledge. Experimental results on a speaker identification task illustrate the ability of the combined bottom-up top-down system to significantly outperform traditional bottom-up only missing data techniques for various types of mask corruption.
Year
DOI
Venue
2012
10.1142/S0218001412500073
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Missing feature theory, missing data recognition, time-frequency masking, iterative group selection
Data mining,Normalization (statistics),Computer science,A priori and a posteriori,Robustness (computer science),Artificial intelligence,Decision process,Missing data,Probabilistic logic,Pattern recognition,Group selection,Time–frequency analysis,Machine learning
Journal
Volume
Issue
ISSN
26
4
0218-0014
Citations 
PageRank 
References 
0
0.34
22
Authors
2
Name
Order
Citations
PageRank
Daniel Pullella100.68
Roberto Togneri281448.33