S. Narayanan

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This paper proposes and evaluates a new statistical discrimination measure for hidden Markov models (HMMs) extending the notion of divergence, a measure of average discrimination information originally defined for two probability density functions. Similar distance measures have been proposed for the case of HMMs, but those have focused primarily on the(More)
Unsupervised speaker indexing sequentially detects points where a speaker identity changes in a multispeaker audio stream, and categorizes each speaker segment, without any prior knowledge about the speakers. This paper addresses two challenges: The first relates to sequential speaker change detection. The second relates to speaker modeling in light of the(More)
This paper presents a general histogram based divergence estimator based on data-dependent partition. Sufficient conditions for the universal strong consistency of the data-driven divergence estimator, using Lugosi and Nobel's combinatorial notions for partition families, are presented. As a corollary this result is particularized for the emblematic case of(More)
The problem of minimum probability of error signal representation (MPE-SR) considering issues of finite training data is revisited and extended in this paper. Results are presented that justify addressing this problem as a complexity-regularized optimization criterion, reflecting the well-known tradeoff between signal representation quality and learning(More)
Object-oriented programming (OOP) has been revolutionizing software development and maintenance. When applied to simulation of manufacturing systems, OOP also provides an opportunity for developing new ways of thinking and modeling. In this paper, we identify existing large-scale, persistent OOP-based research efforts focusing on manufacturing system(More)
This paper introduces the emerging area of web-based simulations and presents an overview of the opportunities and challenges in this field. This introduction begins with an outline of the World Wide Web and aspects of simulation impacted by advances on the Internet. Next, various types of applications of web-based simulations are illustrated. This article(More)
Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension process with Bayesian decision trees. In this paper we extend the Narayanan and Jurafsky model to make further predictions about reading time given the probability of difference parses(More)