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» Using hidden Markov models and wavelets for face recognition
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ICDAR
2009
IEEE
14 years 2 months ago
Unsupervised HMM Adaptation Using Page Style Clustering
In this paper we present an innovative two-stage adaptation approach for handwriting recognition that is based on clustering of similar pages in the training data. In our approach...
Huaigu Cao, Rohit Prasad, Shirin Saleem, Premkumar...
ICPR
2006
IEEE
14 years 8 months ago
Mixture of Support Vector Machines for HMM based Speech Recognition
Speech recognition is usually based on Hidden Markov Models (HMMs), which represent the temporal dynamics of speech very efficiently, and Gaussian mixture models, which do non-opt...
Sven E. Krüger, Martin Schafföner, Marce...
ICPR
2006
IEEE
14 years 8 months ago
Detecting Coarticulation in Sign Language using Conditional Random Fields
Coarticulation is one of the important factors that makes automatic sign language recognition a hard problem. Unlike in speech recognition, coarticulation effects in sign language...
Ruiduo Yang, Sudeep Sarkar
DAS
2008
Springer
13 years 9 months ago
Writer-Dependent Recognition of Handwritten Whiteboard Notes in Smart Meeting Room Environments
In this paper we present a writer-dependent handwriting recognition system based on hidden Markov models (HMMs). This system, which has been developed in the context of research o...
Marcus Liwicki, Andreas Schlapbach, Horst Bunke
ICASSP
2010
IEEE
13 years 2 months ago
Unsupervised knowledge acquisition for Extracting Named Entities from speech
This paper presents a Named Entity Recognition (NER) method dedicated to process speech transcriptions. The main principle behind this method is to collect in an unsupervised way ...
Frédéric Béchet, Eric Charton