Optimal Data Fusion in Multiple Sensor Detection Systems

Chair, Z.; Varshney, Pramod K.

doi:10.1109/taes.1986.310699

Cited by 1,009 publications

(524 citation statements)

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“…i.e., the posterior probability of the combined classifier is the average posterior probability of the single stream classifiers (Chair and Varshney, 1986). In our case, only some of the single stream classifiers are statistical and produce posterior probabilities, namely, salient lexical and context information classifiers, as can be seen in Eqs.…”

Section: Fusion Of Acoustic Lexical and Contextual Informationmentioning

confidence: 99%

Detecting emotional state of a child in a conversational computer game

Yıldırım

Narayanan

Potamianos

2011

Computer Speech & Language

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Section: Fusion Of Acoustic Lexical and Contextual Informationmentioning

confidence: 99%

Detecting emotional state of a child in a conversational computer game

Yıldırım

Narayanan

Potamianos

2011

Computer Speech & Language

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“…The network aims to minimize the probability of error or some other cost function at the fusion center, by choosing optimal transmission functions and fusion rules. Various properties and variants of the decentralized detection problem in a parallel configuration have been extensively studied over the last twenty-five years; examples include the following: [4][5][6][7][8] study the properties of optimal fusion rules and quantizers at sensor nodes; [9] shows the existence of optimal strategies, and proves that likelihood ratio quantizers are optimal for a large class of problems including the decentralized detection problem; and [10][11][12][13][14] consider constrained decentralized detection. The reader is referred to [15,16] for a survey of the work done in this area.…”

Section: Introductionmentioning

confidence: 99%

Error Exponents for Decentralized Detection in Tree Networks

Tay

Tsitsiklis

2008

Networked Sensing Information and Control

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Summary. We consider the problem of decentralized detection in a sensor network consisting of nodes arranged as a tree of bounded height. We characterize the optimal error exponent under a Neyman-Pearson formulation, and show that the Type II error probability decays exponentially with the number of nodes. Surprisingly, the optimal error exponent is often the same as that corresponding to a parallel configuration. We provide sufficient, as well as necessary, conditions for this to happen. We also consider the impact of failure-prone sensors or unreliable communications between sensors on the detection performance. Simple strategies that nearly achieve the asymptotically optimal performance in these cases are also developed.

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“…Using and , we estimate (where is in word and is the corresponding phoneme from the phone recognizer) and . Using Bayes theorem, we find and Hence, a correct/incorrect likelihood ratio for the co-occurrence of a pair of phones and corresponding to the word can be defined (8) (Note that a similar approach of using a likelihood ratio based on a cross-confusion matrix was taken in [12].) Again assuming independence of terms, a confidence measure for word is then (9) where is estimated as the logarithm of the ratio of correct words to incorrect words.…”

Section: A Alignment and Comparison Methodsmentioning

confidence: 99%

High-level approaches to confidence estimation in speech recognition

Cox

Dasmahapatra

2002

IEEE Trans. Speech Audio Process.

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Abstract-We describe some high-level approaches to estimating confidence scores for the words output by a speech recognizer. By "high-level" we mean that the proposed measures do not rely on decoder specific "side information" and so should find more general applicability than measures that have been developed for specific recognizers. Our main approach is to attempt to decouple the language modeling and acoustic modeling in the recognizer in order to generate independent information from these two sources that can then be used for estimation of confidence. We isolate these two information sources by using a phone recognizer working in parallel with the word recognizer. A set of techniques for estimating confidence measures using the phone recognizer output in conjunction with the word recognizer output is described. The most effective of these techniques is based on the construction of "metamodels," which generate alternative word hypotheses for an utterance. An alternative approach requires no other recognizers or extra information for confidence estimation and is based on the notion that a word that is semantically "distant" from the other decoded words in the utterance is likely to be incorrect. We describe a method for constructing "semantic similarities" between words and hence estimating a confidence. Results using the U.K. version of the Wall Street Journal are given for each technique.

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Optimal Data Fusion in Multiple Sensor Detection Systems

Cited by 1,009 publications

References 5 publications

Detecting emotional state of a child in a conversational computer game

Detecting emotional state of a child in a conversational computer game

Error Exponents for Decentralized Detection in Tree Networks

High-level approaches to confidence estimation in speech recognition

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