Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

This paper discusses a probabilistic on-line handwriting recognition scheme, based on Hidden Markov Models (HMM's), and its implementation for recognizing handwritten words captured from a tablet. Statistical methods, such as HMM's have been used successfully for speech recognition. These methods have recently been applied to the problem of handwriting recognition as well. This paper, discusses...

We address the problem of robust lip tracking, visual speech feature extraction, and sensor integration for audiovisual speech recognition applications. An appearance based model of the articulators, which represents linguistically important features, is learned from example images and is used to locate, track, and recover visual speech information. We tackle the problem of joint temporal model...