Manuel Reyes-Gomez

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Detailed hidden Markov models (HMMs) that capture the constraints implicit in a particular sound can be used to estimate obscured or corrupted portions from partial observations, the situation encountered when trying to identify multiple, overlapping sounds. However, when the complexity and variability of the sounds are high, as in a particular speaker's(More)
Hidden Markov Models (HMMs) permit a natural and flexible way to model time-sequential data. The ease of concatenation and timewarping algorithms implementation on HMMs suit them very well for segmentation and content based audio classification applications, as is clear from their extended and successful use on speech recognition applications. Speech has a(More)
In tandem acoustic modeling, signal features are first processed by a discriminantly-trained neural network, then the outputs of this network are treated as the feature inputs to a conventional distribution-modeling Gaussian-mixture model (GMM) speech recognizer. This arrangement achieves relative error rate reductions of 30% or more on the Aurora task, as(More)
MOTIVATION AND RESULTS Motivated by the ability of a simple threading approach to predict MHC I--peptide binding, we developed a new and improved structure-based model for which parameters can be estimated from additional sources of data about MHC-peptide binding. In addition to the known 3D structures of a small number of MHC-peptide complexes that were(More)
In this paper we present a new speaker-separation algorithm for separating signals with known statistical characteristics from mixed multi-channel recordings. Speaker separation has conventionally been treated as a problem of Blind Source Separation (BSS). This approach does not utilize any knowledge of the statistical characteristics of the signals to be(More)
The major histocompatibility complex (MHC) plays important roles in the workings of the human immune system. Specificity of MHC binding to peptide fragments from cellular and pathogens' proteins has been found to correlate with disease outcome and pathogen or cancer evolution. In this paper we propose a novel approach to predicting binding configurations(More)
Speech and other natural sounds show high temporal correlation and smooth spectral evolution punctuated by a few, irregular and abrupt changes. In a conventional Hidden Markov Model (HMM), such structure is represented weakly and indirectly through transitions between explicit states representing ‘steps’ along such smooth changes. It would be more efficient(More)
Speaker models for blind source separation are typically based on HMMs consisting of vast numbers of states to capture source spectral variation, and trained on large amounts of isolated speech. Since observations can be similar between sources, inference relies on sequential constraints from the state transition matrix which are, however, quite weak. To(More)
Speaker separation has conventionally been treated as a problem of Blind Source Separation (BSS). This approach does not utilize any knowledge of the statistical characteristics of the signals to be separated, relying mainly on the independence between the various signals to separate them. Maximum-likelihood techniques, on the other hand, utilize knowledge(More)
Tandem acoustic modeling consists of taking the outputs of a neural network discriminantly trained to estimate the phone-class posterior probabilities of speech, and using them as the input features of a conventional distribution-modeling Gaussian mixture model (GMM) speech recognizer, thereby employing two acoustic models in tandem. This structure reduces(More)