Publication

Boosting word error rates

2005
Conference Papers

Abstract

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample error for sentence examples related to the word error rate. Furthermore, for each sentence example we define a probability distribution in time that represents our belief that an error has been made at that particular frame. This is used to weigh the frames of each sentence in the boosting framework. We present preliminary results on the well-known Numbers 95 database that indicate the importance of this temporal probability distribution.

Official source

https://infoscience.epfl.ch/entities/publication/dc0c483d-f271-4b39-a172-4e3045a99a53

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2005
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/dc0c483d-f271-4b39-a172-4e3045a99a53

About this result

Related concepts (32)

Boosting (machine learning)

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Gradient boosting

Gradient boosting is a machine learning technique used in regression and classification tasks, among others. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest.

Related publications (32)

Boosting word error rates

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