Recognition of sign language subwords based on boosted hidden Markov models

Sign language recognition (SLR) plays an important role in human-computer interaction (HCI), especially for the convenient communication between deaf and hearing society. How to enhance the traditional hidden Markov models (HMM) based SLR is an important issue in the SLR community. And how to refine the boundaries of the classifiers to effectively characterize the property of spread-out of the training samples is another significant issue. In this paper, a new classification framework applying adaptive boosting (AdaBoost) strategy to continuous HMM (CHMM) training procedure at the subwords classification level for SLR is presented. The ensemble of multiple composite CHMMs for each subword trained in boosting iterations tends to concentrate more on the hard-to-classify samples so as to generate more complex decision boundary than that of the single HMM classifier. Experimental results on the vocabulary of frequently used Chinese sign language (CSL) subwords show that the proposed boosted CHMM outperforms the conventional CHMM for SLR

The application of manifold based visual speech units for visual speech recognition

This dissertation presents a new learning-based representation that is referred to as a Visual Speech Unit for visual speech recognition (VSR). The automated recognition of human speech using only features from the visual domain has become a significant research topic that plays an essential role in the development of many multimedia systems such as audio visual speech recognition(AVSR), mobile phone applications, human-computer interaction (HCI) and sign language recognition. The inclusion of the lip visual information is opportune since it can improve the overall accuracy of audio or hand recognition algorithms especially when such systems are operated in environments characterized by a high level of acoustic noise. The main contribution of the work presented in this thesis is located in the development of a new learning-based representation that is referred to as Visual Speech Unit for Visual Speech Recognition (VSR). The main components of the developed Visual Speech Recognition system are applied to: (a) segment the mouth region of interest, (b) extract the visual features from the real time input video image and (c) to identify the visual speech units. The major difficulty associated with the VSR systems resides in the identification of the smallest elements contained in the image sequences that represent the lip movements in the visual domain. The Visual Speech Unit concept as proposed represents an extension of the standard viseme model that is currently applied for VSR. The VSU model augments the standard viseme approach by including in this new representation not only the data associated with the articulation of the visemes but also the transitory information between consecutive visemes. A large section of this thesis has been dedicated to analysis the performance of the new visual speech unit model when compared with that attained for standard (MPEG- 4) viseme models. Two experimental results indicate that: 1. The developed VSR system achieved 80-90% correct recognition when the system has been applied to the identification of 60 classes of VSUs, while the recognition rate for the standard set of MPEG-4 visemes was only 62-72%. 2. 15 words are identified when VSU and viseme are employed as the visual speech element. The accuracy rate for word recognition based on VSUs is 7%-12% higher than the accuracy rate based on visemes

A boosted multi-HMM classifier for recognition of visual speech elements

A novel boosted classifier using multiple Hidden Markov Models (HMMs) is reported in this paper. The composite HMMs are specially trained to highlight certain group of training samples with the application of adaptive boosting technique. Experiments were carried out to identify the basic visual speech elements in English using the proposed boosted classifier. Comparing the results obtained using the proposed classifier and those obtained using the traditional single HMM classifier, it may be said that the proposed system is significantly better in terms of accuracy and robustness.Accepted versio

A boosted multi-HMM classifier for recognition of visual speech elements

A boosted multi-HMM classifier for recognition of visua