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An Introduction to the Chinese Speech Recognition Front-End of the NICT/ATR Multi-Lingual Speech Translation System

Tsinghua Science and Technology 2008年第4期13卷 545-552页

作者：张劲松 Takatoshi Jitsuhiro Hirofumi Yamamoto 胡新辉 Satoshi Nakamura Knowledge Creating Communication Research Center National Institute of Information and Communications Technology 2-2-2 Keihanna Science City Kyoto 619-0288 Japan ATR Spoken Language Translation Research Laboratories 2-2-2 Keihanna Science City Kyoto 619-0288 Japan ATR Knowledge Science Laboratories 2-2-2 Keihanna Science City Kyoto 619-0288 Japan Knowledge Creating Communication Research Center National Institute of Information and Communications Technology 2-2-2 Keihanna Science City Kyoto 619-0288 Japan

This paper introduces several important features of the Chinese large vocabulary continuous speech recognition system in the NICT/ATR multi-lingual speech-to-speech translation system. The features include：（1） a flexible way to derive an information rich phoneme set based on mutual information between a text corpus and its phoneme set; （2） a hidden Markov network acoustic model and a successive state splitting algorithm to generate its model topology based on a minimum description length criterion; and （3） advanced language modeling using multi-class composite N-grams. These features allow a recognition performance of 90% character accuracy in tourism related dialogue with a real time response speed.

关键词： Chinese speech recognition mutual information phoneme set design hidden Markov network minimum description length successive state splitting multi-class composite N-grams

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An information theory perspective on computational vision

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Frontiers of Electrical and Electronic Engineering in China 2010年第3期5卷 329-346页

作者： Alan YUILLE Department of Statistics University of California at Los AngelesLos AngelesCA 90095USA

This paper introduces computer vision from an information theory *** discuss how vision can be thought of as a decoding problem where the goal is to find the most efficient encoding of the visual *** requires probabilistic models which are capable of capturing the complexity and ambiguities of natural *** start by describing classic Markov Random Field(MRF)models of *** stress the importance of having efficient inference and learning algorithms for these models and emphasize those approaches which use concepts from information *** we introduce more powerful image models that have recently been developed and which are better able to deal with the complexities of natural *** models use stochastic grammars and hierarchical *** are trained using images from increasingly large ***,we described how techniques from information theory can be used to analyze vision models and measure the effectiveness of different visual cues.

关键词： computer vision pattern recognition information theory minimum description length Markov random field(MRF)model stochastic grammars

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