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Modeling phonological units of speech is a critical issue in speech recognition. In this paper, our recent development of an overlapping-feature-based phonological model that represents long-span contextual dependency in speech acoustics is reported. In this model, high-level linguistic constraints are incorporated in automatic construction of the patterns(More)
Spinal cord injury (SCI) is one of the most disabling diseases. Cell-based gene therapy is becoming a major focus for the treatment of SCI. Bone marrow-derived mesenchymal stem cells (BMSCs) are a promising stem cell type useful for repairing SCI. However, the effects of BMSCs transplants are likely limited because of low transplant survival after SCI.(More)
A new, data-driven approach to deriving overlapping articulatory-feature based HMMs for speech recognition is presented in this paper. This approach uses speech data from University of Wisconsin's Microbeam X-ray Speech Production Database. Regression tree models were created for constructing HMMs. Use of actual articulatory data improves upon our previous(More)
Tracking-by-detection methods have been widely studied and some promising results have been obtained. These methods use discriminative appearance models to train and update online classifiers. They also use a sliding window to detect samples which will then be classified. Then, the location of the sample with the maximum classifier response will be selected(More)
Bone marrow mesenchymal stem cells (BMSCs) have the ability of self-renewal and multi-directional differentiation. Recent reports showed that BMSCs could differentiate into endocrine cells of pancreas. However, the differentiation is not efficient enough to produce insulin-producing cells for the future therapeutic use. Pdx-1 is a crucial regulator for(More)
Language-independent embedded speech recognition is a necessary and important application. Considering personal privacy, collection difficulty of all the reference words, and limited storage space of mobile devices, language-independent (LI) embedded speech recognition should be classified into lightweight speaker-dependent (SD) cases. Dynamic time warping(More)
We describe a robust speech understanding system based on our newly developed approach to spoken language processing. We show that a robust NLU system can be rapidly developed using a relatively simple speech recognizer to provide sufficient information for database retrieval by spoken language. Our experimental system consists of three components: a speech(More)
Grammar-based speech recognition systems exhibit performance degradation as their vocabulary sizes increase. Data clustering is deemed to reduce the proportionality of this problem. We introduce an approach to data clustering for automatic speech recognition systems using kohonen self-organized map. Clustering results are used further to build a language(More)
Modeling phonological units of speech is a critical issue in speech recognition. In this paper, we report our recent development o f a n o v erlapping feature-based phonologi-cal model which gives long-span contextual dependency. We extend our earlier work by incorporating high-level linguistic constaints in automatic construction of the feature overlapping(More)