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In human speech, most boundaries between phones/words are fuzzy. If a time slice which only includes a sole boundary is given, it is possible that the boundary may locate at any frame within the slice. Different boundary locations form several potential observation segments, which should have similar acoustic spaces because of their neighboring trait in time domain. We call them neighboring segments. In this paper, a fast algorithm of parallel processing of neighboring segments is proposed for decoding. Since the decoder can search a bigger pruning threshold in parallel processing, the proposed algorithm is faster than decoding a single segment. This algorithm is successfully integrated into a Segment Model (SM) based Mandarin Large Vocabulary Continuous Speech Recognition (LVCSR) system, and saves approximately 50% decoding time without obvious influence on the recognition accuracy. [ABSTRACT FROM AUTHOR]

It is a important issue that luminance coherent in the sequence video. After adjusting in the video, and then we can see the clearly image with accordant luminance. In addition, inpainting the intensity in image or video, the image will display the new intensity. We can see the image different and get better than original intensity. Moreover, we maybe also adjust intensity contrast, make sure the detail color and luminance appear same coordinate color. In these issue, we try to use average intensity to solve luminance in video. We can calculate the middle intensity with the frames average intensity, and then we get the threshold help us for fixing the image intensity. And we can use histogram and tensor voting to solve luminance difference in image. We will show the experiment in this research. Then, we will compare these two methods, the result in histogram and tensor voting. [ABSTRACT FROM AUTHOR]