Publication

A Novel Criterion for Classifiers Combination in Multistream Speech Recognition

2009
Journal Articles

Abstract

In this paper we propose a novel information theoretic criterion for optimizing the linear combination of classifiers in multi stream automatic speech recognition. We discuss an objective function that achieves a trade-off between the minimization of a bound on the Bayes probability of error and the minimization of the divergence between the individual classifier outputs and their combination. The method is compared with the conventional inverse entropy and minimum entropy combinations on both small and large vocabulary automatic speech recognition tasks. Results reveal that it outperforms other linear combination rules. Furthermore we discuss the advantages of the proposed approach and the extension to other (non-linear) combination rules.

Official source

https://infoscience.epfl.ch/entities/publication/ef35541f-9593-4f0f-8d7c-be8651ef24ad

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2009
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/ef35541f-9593-4f0f-8d7c-be8651ef24ad

About this result

Related concepts (32)

Linear subspace

In mathematics, and more specifically in linear algebra, a linear subspace or vector subspace is a vector space that is a subset of some larger vector space. A linear subspace is usually simply called a subspace when the context serves to distinguish it from other types of subspaces. If V is a vector space over a field K and if W is a subset of V, then W is a linear subspace of V if under the operations of V, W is a vector space over K.

Naive Bayes classifier

In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.

Linear independence

In the theory of vector spaces, a set of vectors is said to be if there exists no nontrivial linear combination of the vectors that equals the zero vector. If such a linear combination exists, then the vectors are said to be . These concepts are central to the definition of dimension. A vector space can be of finite dimension or infinite dimension depending on the maximum number of linearly independent vectors. The definition of linear dependence and the ability to determine whether a subset of vectors in a vector space is linearly dependent are central to determining the dimension of a vector space.

Related publications (32)

A Novel Criterion for Classifiers Combination in Multistream Speech Recognition

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