Prior knowledge guided maximum expected likelihood based model selection and adaptation for nonnative speech recognition

2007 ◽  
Vol 21 (2) ◽  
pp. 247-265 ◽  
Author(s):  
Xiaodong He ◽  
Yunxin Zhao
Keyword(s):  
Speech Recognition ◽  
Model Selection ◽  
Prior Knowledge

Effects of aging on the ability to benefit from prior knowledge of message content in masked speech recognition

2012 ◽  
Vol 54 (4) ◽  
pp. 529-542 ◽  
Author(s):  
Meihong Wu ◽  
Huahui Li ◽  
Zhiling Hong ◽  
Xinchi Xian ◽  
Jingyu Li ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Prior Knowledge ◽  
Message Content ◽  
Effects Of Aging

scholarly journals Machine Learning in Morphological Segmentation

2009 ◽  
pp. 320-334
Author(s):  
O. Lezoray ◽  
G. Lebrun ◽  
C. Meurie ◽  
C. Charrier ◽  
A. Elmotataz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Prior Knowledge ◽  
Mathematical Morphology ◽  
Learning Approaches ◽  
Watershed Segmentation ◽  
Microscopic Images ◽  
Morphological Segmentation ◽  
Multiple Classifier

The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis. Mathematical morphology is a very well established theory to process images. Segmentation by morphological means is based on watershed that considers an image as a topographic surface. Watershed requires input and marker image. The user can provide the latter but far more relevant results can be obtained for watershed segmentation if marker extraction relies on prior knowledge. Parameters governing marker extraction varying from image to image, machine learning approaches are of interest for robust extraction of markers. We review different strategies for extracting markers by machine learning: single classifier, multiple classifier, single classifier optimized by model selection.

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