Publications related to Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Acoustic models for posterior features in speech recognition

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

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

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

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

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

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 ...

IDIAP2007

Identifying unexpected words using in-context and out-of-context phoneme posteriors

Hynek Hermansky, Hamed Ketabdar

The paper proposes and discusses a machine approach for identification of unexpected (zero or low probability) words. The approach is based on use of two parallel recognition channels, one channel employing sensory information from the speech signal togeth ...

IDIAP2006

Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator

Graph Chatbot

Chat with Graph Search

Acoustic models for posterior features in speech recognition

Acoustic Models for Posterior Features in Speech Recognition

Acoustic Models for Posterior Features in Speech Recognition

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

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

Support Vector Machines with a Reject Option

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

On Confusions in a Phoneme Recognizer

On Confusions in a Phoneme Recognizer

Identifying unexpected words using in-context and out-of-context phoneme posteriors

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

Acoustic Models for Posterior Features in Speech Recognition

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

Comparing Different Word Lattice Rescoring Approaches Towards Keyword Spotting

On Confusions in a Phoneme Recognizer

Acoustic models for posterior features in speech recognition

Support Vector Machines with a Reject Option

Acoustic Models for Posterior Features in Speech Recognition

On Confusions in a Phoneme Recognizer

Identifying unexpected words using in-context and out-of-context phoneme posteriors