In this work we show how conditional mean imputation can be bounded through the use of
box-truncated Gaussian distributions. That is of interest when signals or features are partly …

This paper addresses the problem of feature compensation in the log-spectral domain by
using the missing-data (MD) approach to noise robust speech recognition, that is, the log …

A speech denoising method based on Non-Negative Matrix Factorization (NMF) is
presented in this paper. With respect to previous related works, this paper makes two …

In this thesis, a new approach to estimation problems under the presence of bounded
uncertain parameters and statistical noise has been presented. The objective is to use the …

In missing feature based automatic speech recognition (ASR), the role of the spectro-
temporal mask in providing an accurate description of the relationship between target …

An effective way to increase noise robustness in automatic speech recognition (ASR)
systems is feature enhancement based on an analytical distortion model that describes the …

This paper addresses the problem of feature compensation in the log-spectral domain for
speech recognition in noise by recasting the speech distortion problem as an occlusion one …

Automatic speech recognition (ASR) performance degrades rapidly when speech is
corrupted with increasing levels of noise. Missing-data techniques are a family of methods …

This paper proposes a novel compensation technique developed in the log-spectral domain.
Our proposal consists in a minimum mean square error (MMSE) estimator derived from an …

Cette thèse porte sur l'estimation d'état des systèmes dynamiques à temps discret dans le
contexte de l'intégration d'incertitudes statistiques et à erreurs bornées. Partant du filtre de …