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This paper presents a new technique for preparing word templates to improve the performance of dynamic time warping based keyword spotting. The proposed technique selects one reference template from a small set of examples and in contrast to existing model based approaches does not require extensive training
Keyword (Feature) selection enhances and improves many Information Retrieval (IR) tasks such as document categorization, automatic topic discovery, etc. The problem of keyword selection is usually solved using supervised algorithms. In this paper, we propose an unsupervised approach that combines keyword selection and
Automatic image annotation is a promising key to semantic-based image retrieval by keywords. Most existing automatic image annotation approaches focused on exploring the relationship between images and annotation words and neglected the semantic information of the annotated keywords. In this paper we propose a semi
keywords in common, then the image is added to an image repository. Additional meta-information are now associated with each image such as caption, cluster features, names of people in the news article, etc. A very large repository containing more than 983k images from 12 million news articles was built using this approach
and converts it into routing keywords. Accent identification is the most important factor for improving the performance of natural language call-routing systems because accents vary widely, even within the same country or community. This variation occurs when non-native speakers start to learn a second language; the
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