Concept

Inverse probability

Related publications (64)

Support Vector Machines with a Reject Option

We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow’s rule, is deﬁned by two thresholds on posterior probabilities. From simple des ...

2008

Combining Evidence from a Generative and a Discriminative Model in Phoneme Recognition

Hynek Hermansky, Joel Praveen Pinto

We investigate the use of the log-likelihood of the features obtained from a generative Gaussian mixture model, and the posterior probability of phonemes from a discriminative multilayered perceptron in multi-stream combination for recognition of phonemes. ...

2008

Combining Evidence from a Generative and a Discriminative Model in Phoneme Recognition

Hynek Hermansky, Joel Praveen Pinto

IDIAP2008

Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Hynek Hermansky, Joel Praveen Pinto

We present a framework to apply Volterra series to analyze multilayered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. The identified Volterra kernels reveal the spectro-temporal patterns that are l ...

Idiap2008

Using KL-based Acoustic Models in a Large Vocabulary Recognition Task

Hervé Bourlard, Guillermo Aradilla

Posterior probabilities of sub-word units have been shown to be an effective front-end for ASR. However, attempts to model this type of features either do not benefit from modeling context-dependent phonemes, or use an inefficient distribution to estimate ...

IDIAP2008

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

Hervé Bourlard, Hynek Hermansky, Joel Praveen Pinto

In this paper, we further investigate the large vocabulary continuous speech recognition approach to keyword spotting. Given a speech utterance, recognition is performed to obtain a word lattice. The posterior probability of keyword hypotheses in the latti ...

IDIAP2007

A mixture model for multivariate extremes

Anthony Christopher Davison, Marc-Olivier Boldi

The spectral density function plays a key role in ﬁtting the tail of multivariate extremal data and so in estimating probabilities of rare events. This function satisﬁes moment constraints but unlike the univariate extreme value distributions has no simple ...

2007

On Confusions in a Phoneme Recognizer

Hynek Hermansky, Joel Praveen Pinto

In this paper, we analyze the confusions patterns at three places in the hybrid phoneme recognition system. The confusions are analyzed at the pronunciation, the posterior probability, and the phoneme recognizer levels. The confusions show significant stru ...

2007

On Confusions in a Phoneme Recognizer

Hynek Hermansky, Joel Praveen Pinto

IDIAP2007

An Acoustic Model Based on Kullback-Leibler Divergence for Posterior Features

Hervé Bourlard, Guillermo Aradilla

This paper investigates the use of features based on posterior probabilities of subword units such as phonemes. These features are typically transformed when used as inputs for a hidden Markov model with mixture of Gaussians as emission distribution (HMM/G ...

2007

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