This paper surveys a new research field of object behavior classification using sequential syntactic pattern recognition, which recognizes high-level object behaviors while in parallel recovering from low-level object recognition classification errors. A new approach of syntactical object behavior classification with a robust implementation is introduced. It is an innovative approach that requi...

Differences in human vocal tract lengths can cause inter speaker acoustic variability in speech signals spoken by different speakers for the same textual version and due to these variations, the robustness of a speaker independent (SI) speech recognition system is affected. Speaker normalization using vocal tract length normalization (VTLN) is an effective approach to reduce the affect of these...

Neural networks (NNs), such as multi layer perceptrons and radial basis function architectures, proved to be powerful tools in many problems where the objective is robust classification. However, in applications that require simultaneous segmentation and recognition, such as speech and handwriting recognition, NNs were used with much less success. In this work, we introduce an architecture for ...

This paper describes a study on using explicit duration models in hidden Markov model (HMM) based Cantonese connecteddigit recognition. An HMM does not give explicit control to the temporal structure of speech. As a result, the recognition output may exhibit unreasonable duration pattern, which is often accompanied with the presence of recognition errors. We propose to use a duration model that...

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 ...

The SVM is a new classification technique in the field of statistical learning theory which has been applied with success in pattern recognition applications like face and speaker recognition, while the HMM has been found to be a powerful statistical technique which is applied to handwriting recognition and signature verification. This paper reports on a comparison of the two classifiers in off...

In this paper we present neuro-electric interfaces for virtual device control. The examples presented rely upon sampling Electromyogram data from a participants forearm. This data is then fed into pattern recognition software that has been trained to distinguish gestures from a given gesture set. The pattern recognition software consists of hidden Markov models which are used to recognize the g...

Automatic recognition of musical patterns plays a crucial part in Musicological and Ethno musicological research and can become an indispensable tool for the search and comparison of music extracts within a large multimedia database. This paper finds an efficient method for recognizing isolated musical patterns in a monophonic environment, using Hidden Markov Model. Each pattern, to be recogniz...

The sign language recognition is the most popular research area involving computer vision, pattern recognition and image processing. It enhances communication capabilities of the mute person. In this paper, I present an object based key frame selection. Forward Algorithm is used for shape similarity for one and two handed gesture recognition. That recognition is with feature and without feature...

the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...