Takemaru-kun system is a practical speech-oriented guidance system developed to examine spoken interface through longterm operation in a public place that collected natural humanmachine interaction data. In 2004 the f...
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Takemaru-kun system is a practical speech-oriented guidance system developed to examine spoken interface through longterm operation in a public place that collected natural humanmachine interaction data. In 2004 the following advances improving reliability of the system were introduced, which conduced acquiring positive increase of access from users: (1) Rejection of unintended speech based on Gaussian Mixture Models (GMMs);(2) Removal of short, unnecessary inputs of impulsive noise;(3) Child or adult user discrimination;(4) Web-based monitoring mechanisms. This paper summarizes the Takemaru-kun system and analysis of 177,789 data collected by two-years actual operation. Experiments with the collected data proved that a combination of GMM-based verification and short input removal can excise 85% of the invalid inputs, including laughter, incomprehensible utterances, and even some background utterances.
Authors have developed a novel method for achieving higher recognition capability of a 3-D face recognition system based on feature line method, which is called Modified Nearest Feature Line. Combined with our develop...
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Authors have developed a novel method for achieving higher recognition capability of a 3-D face recognition system based on feature line method, which is called Modified Nearest Feature Line. Combined with our developed view-based Karhunen-Loeve transformation method as a feature extraction subsystem, the Modified Nearest Feature Line method is used as a classifier to build a 3-D face recognition system. As recognition rates are usually decreased by increasing the number of object classes, the authors evaluate and analyze the performance of this Modified Nearest Feature Line method for recognizing 3-D face images and compared with that of the conventional Nearest Feature Line method. In our experiments, each object class consists of images of persons with their viewpoint positions and expressions. Experimental results show that increasing the number of object classes influenced the recognition rates of both systems. However, the decrement slopes of the recognition system using Modified Nearest Feature Line method were lower than that of using Nearest Feature Line method. It is also shown that at every same number of persons to be recognized, our Modified Nearest Feature Line method always gave a high recognition rate than the original Nearest Feature Line method, with up to 20% in recognition rate difference.
This paper describes effectiveness in applying speaker normalization based on a vocal tract length ratio between two speakers to spoken word recognition. One of the two speakers is a speaker who utters unknown words t...
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Sell-Generating Neural Networks (SGNN) have the feature of fast processing by automatically constructing a self-generating neural tree (SGNT) from a given training data set. The prediction accuracy of SGNNs for chaoti...
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Learning by teaching has been extensively studied in education and psychology research. However, there has been relatively less effort in this research avenue in CSCL research. computer supported teaming by teaching, ...
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ISBN:
(纸本)0805857826
Learning by teaching has been extensively studied in education and psychology research. However, there has been relatively less effort in this research avenue in CSCL research. computer supported teaming by teaching, as a genus of CSCL activities, is expected to draw more attention in the future. In this pilot study, we propose a model of computer supported teaming by teaching that involves peer tutors in preparing their instruction notes and teaching their peer tutees verbally. Collaborative teaming is incorporated in our pedagogical design. As an initial investigation and for future improvement of our design, we conducted two experimental trials in a graduate-level course. This paper discusses the design of this model and reports our findings from the experimental trials.
As the power of computational Grids increases, there is a corresponding need for better usability in PSE community. In this paper, we describe our experiences on design of project GridPSi, a Gridenabled PSE for physic...
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Bioinformatics nowadays is a very attractive field. Many fascinating biological problems were still unsolved, even after a great amount of diverse genomic sequences have been sequenced for the coming of post genome er...
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ExploreCSEd is a collaborative project funded by the HE Academy - information and computersciences. The aim of the project is to investigate the skills and difficulties involved in learning to program by gathering da...
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ISBN:
(纸本)1595930248
ExploreCSEd is a collaborative project funded by the HE Academy - information and computersciences. The aim of the project is to investigate the skills and difficulties involved in learning to program by gathering data from students and educators in multiple institutions and bringing these together for analysis.
It has already been proven that peer tutoring is an effective way to engage students in learning. However, without technology support, it can be difficult to conduct peer tutoring program in a classroom. We propose a ...
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The era of 1: 1 educational computing environment where each student has one mobile computing device is not far away. When such technology designed for individuals is applied to group learning, several student groupin...
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ISBN:
(纸本)0805857826
The era of 1: 1 educational computing environment where each student has one mobile computing device is not far away. When such technology designed for individuals is applied to group learning, several student grouping problems could be encountered. In this paper, three issues are identified to illustrate the vision of the 1:2 educational computing environments. In a 1:2 classroom, besides the mobile devices, students also have their own computing desks, i.e. MatrixDesks, to solve the potential student grouping problems. By putting MatrixDesks together, a small group can form a shared working space with the combined desktops immediately and they can use their own digital pens as the input devices to work on and talk over it. Meanwhile, the students use their mobile devices to handle the related individual tasks. MatrixDesks is a coordination of applying the mobile computing and invisible computing to collaborative learning, which will lead to the accomplishment of 1:2 educational computing environments.
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