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 f...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.展开更多
This paper introduces computer vision from an information theory perspective.We discuss how vision can be thought of as a decoding problem where the goal is to find the most efficient encoding of the visual scene.This...This paper introduces computer vision from an information theory perspective.We discuss how vision can be thought of as a decoding problem where the goal is to find the most efficient encoding of the visual scene.This requires probabilistic models which are capable of capturing the complexity and ambiguities of natural images.We start by describing classic Markov Random Field(MRF)models of images.We stress the importance of having efficient inference and learning algorithms for these models and emphasize those approaches which use concepts from information theory.Next we introduce more powerful image models that have recently been developed and which are better able to deal with the complexities of natural images.These models use stochastic grammars and hierarchical representations.They are trained using images from increasingly large databases.Finally,we described how techniques from information theory can be used to analyze vision models and measure the effectiveness of different visual cues.展开更多
This paper outlines a theory of estimation,where optimality is defined for all sizes of data—not only asymptotically.Also one principle is needed to cover estimation of both real-valued parameters and their number.To...This paper outlines a theory of estimation,where optimality is defined for all sizes of data—not only asymptotically.Also one principle is needed to cover estimation of both real-valued parameters and their number.To achieve this we have to abandon the traditional assumption that the observed data have been generated by a“true”distribution,and that the objective of estimation is to recover this from the data.Instead,the objective in this theory is to fit‘models’as distributions to the data in order to find the regular statistical features.The performance of the fitted models is measured by the probability they assign to the data:a large probability means a good fit and a small probability a bad fit.Equivalently,the negative logarithm of the probability should be minimized,which has the interpretation of code length.There are three equivalent characterizations of optimal estimators,the first defined by estimation capacity,the second to satisfy necessary conditions for optimality for all data,and the third by the complete Minimum Description Length(MDL)principle.展开更多
文摘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.
基金The author would like to acknowledge funding support from NSF with grants IIS-0917141 and 0613563 and from AFOSR FA9550-08-1-0489.
文摘This paper introduces computer vision from an information theory perspective.We discuss how vision can be thought of as a decoding problem where the goal is to find the most efficient encoding of the visual scene.This requires probabilistic models which are capable of capturing the complexity and ambiguities of natural images.We start by describing classic Markov Random Field(MRF)models of images.We stress the importance of having efficient inference and learning algorithms for these models and emphasize those approaches which use concepts from information theory.Next we introduce more powerful image models that have recently been developed and which are better able to deal with the complexities of natural images.These models use stochastic grammars and hierarchical representations.They are trained using images from increasingly large databases.Finally,we described how techniques from information theory can be used to analyze vision models and measure the effectiveness of different visual cues.
文摘This paper outlines a theory of estimation,where optimality is defined for all sizes of data—not only asymptotically.Also one principle is needed to cover estimation of both real-valued parameters and their number.To achieve this we have to abandon the traditional assumption that the observed data have been generated by a“true”distribution,and that the objective of estimation is to recover this from the data.Instead,the objective in this theory is to fit‘models’as distributions to the data in order to find the regular statistical features.The performance of the fitted models is measured by the probability they assign to the data:a large probability means a good fit and a small probability a bad fit.Equivalently,the negative logarithm of the probability should be minimized,which has the interpretation of code length.There are three equivalent characterizations of optimal estimators,the first defined by estimation capacity,the second to satisfy necessary conditions for optimality for all data,and the third by the complete Minimum Description Length(MDL)principle.
