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Acoustic models for posterior features in speech recognition
Publications associées (135)
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 ...
Ecole Polytechnique Fédérale de Lausanne2008Acoustic 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 ...
Idiap2008Using 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 ...
IDIAP2008Using Comparison of Parallel Phoneme Probability streams for OOV Word Detection
Mathew Magimai Doss, Hynek Hermansky, Tamara Tosic
In this paper, we investigate the approach of comparing two different parallel streams of phoneme posterior probability estimates for OOV word detection. The first phoneme posterior probability stream is estimated using only the knowledge of short-term acou ...
2008Combining 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. ...
2008Combining 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. ...
IDIAP2008Volterra 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 ...
Idiap2008Bayesian Inference and Optimal Design in the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertain ...
Massachusetts Institute of Technology Press2008Using entropy as a stream reliability estimate for audio-visual speech recognition
Jean-Philippe Thiran, Mihai Gurban
We present a method for dynamically integrating audio-visual information for speech recognition, based on the estimated reliability of the audio and visual streams. Our method uses an information theoretic measure, the entropy derived from the state probab ...
2008Posterior Features Applied to Speech Recognition Tasks with Limited Training Data
Hervé Bourlard, Guillermo Aradilla
This paper describes an approach where posterior-based features are applied in those recognition tasks where the amount of training data is insufficient to obtain a reliable estimate of the speech variability. A template matching approach is considered in ...
IDIAP2008