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Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator
Related publications (34)
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In i-vector based speaker recognition systems, back-end classifiers are trained to factor out nuisance information and retain only the speaker identity. As a result, variabilities arising due to gender, language and accent ( among many others) are suppress ...
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Columbia Business School2011INTEGRATING ARTICULATORY FEATURES USING KULLBACK-LEIBLER DIVERGENCE BASED ACOUSTIC MODEL FOR PHONEME RECOGNITION
In this paper, we propose a novel framework to integrate articulatory features (AFs) into HMM- based ASR system. This is achieved by using posterior probabilities of different AFs (estimated by multilayer perceptrons) directly as observation features in Ku ...
Idiap2011Integrating articulatory features using Kullback-Leibler divergence based acoustic model for phoneme recognition
In this paper, we propose a novel framework to integrate articulatory features (AFs) into HMM- based ASR system. This is achieved by using posterior probabilities of different AFs (estimated by multilayer perceptrons) directly as observation features in Ku ...
2011Investigation of kNN Classifier on Posterior Features Towards Application in Automatic Speech Recognition
Hervé Bourlard, Afsaneh Asaei, Benjamin Picart
Class posterior distributions can be used to classify or as intermediate features, which can be further exploited in different classifiers (e.g., Gaussian Mixture Models, GMM) towards improving speech recognition performance. In this paper we examine the p ...
Idiap2010Support Vector Machines with a Reject Option
We consider the problem of binary classification where the classfier 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 desi ...
Idiap2009Volterra 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 ...
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 ...
IDIAP2008