Publications related to Investigation of kNN Classifier on Posterior Features Towards Application in Automatic Speech Recognition

Multimodal feature extraction and fusion for audio-visual speech recognition

Multimodal signal processing analyzes a physical phenomenon through several types of measures, or modalities. This leads to the extraction of higher-quality and more reliable information than that obtained from single-modality signals. The advantage is two ...

EPFL2009

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 ...

2009

Trajectory analysis using point distribution models

Pierre Roduit

This thesis focuses on the analysis of the trajectories of a mobile agent. It presents different techniques to acquire a quantitative measure of the difference between two trajectories or two trajectory datasets. A novel approach is presented here, based o ...

EPFL2009

Acoustic models for posterior features in speech recognition

Guillermo Aradilla

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...

EPFL2008

Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...

Ecole Polytechnique Fédérale de Lausanne2008

Acoustic Models for Posterior Features in Speech Recognition

Guillermo Aradilla

In this thesis, we investigate the use of posterior probabilities of sub-word units directly as input features for automatic speech recognition (ASR). These posteriors, estimated from data-driven methods, display some favourable properties such as increase ...

Idiap2008

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

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. ...

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

Investigation of kNN Classifier on Posterior Features Towards Application in Automatic Speech Recognition

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Multimodal feature extraction and fusion for audio-visual speech recognition

Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Trajectory analysis using point distribution models

Acoustic models for posterior features in speech recognition

Acoustic Models for Posterior Features in Speech Recognition

Acoustic Models for Posterior Features in Speech Recognition

Support Vector Machines with a Reject Option

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

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

Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Multimodal feature extraction and fusion for audio-visual speech recognition

Acoustic Models for Posterior Features in Speech Recognition

Trajectory analysis using point distribution models

Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Acoustic Models for Posterior Features in Speech Recognition

Support Vector Machines with a Reject Option

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

Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Acoustic models for posterior features in speech recognition

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