97,569 research outputs found
Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models
Conventional deep neural networks (DNN) for speech acoustic modeling rely on
Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary
class labels as the targets for DNN training. Subword classes in speech
recognition systems correspond to context-dependent tied states or senones. The
present work addresses some limitations of GMM-HMM senone alignments for DNN
training. We hypothesize that the senone probabilities obtained from a DNN
trained with binary labels can provide more accurate targets to learn better
acoustic models. However, DNN outputs bear inaccuracies which are exhibited as
high dimensional unstructured noise, whereas the informative components are
structured and low-dimensional. We exploit principle component analysis (PCA)
and sparse coding to characterize the senone subspaces. Enhanced probabilities
obtained from low-rank and sparse reconstructions are used as soft-targets for
DNN acoustic modeling, that also enables training with untranscribed data.
Experiments conducted on AMI corpus shows 4.6% relative reduction in word error
rate
Clustering based on Mixtures of Sparse Gaussian Processes
Creating low dimensional representations of a high dimensional data set is an
important component in many machine learning applications. How to cluster data
using their low dimensional embedded space is still a challenging problem in
machine learning. In this article, we focus on proposing a joint formulation
for both clustering and dimensionality reduction. When a probabilistic model is
desired, one possible solution is to use the mixture models in which both
cluster indicator and low dimensional space are learned. Our algorithm is based
on a mixture of sparse Gaussian processes, which is called Sparse Gaussian
Process Mixture Clustering (SGP-MIC). The main advantages to our approach over
existing methods are that the probabilistic nature of this model provides more
advantages over existing deterministic methods, it is straightforward to
construct non-linear generalizations of the model, and applying a sparse model
and an efficient variational EM approximation help to speed up the algorithm
Aggregation of probabilistic PCA mixtures with a variational-Bayes technique over parameters
International audienceThis paper proposes a solution to the problem of aggre- gating versatile probabilistic models, namely mixtures of probabilistic principal component analyzers. These models are a powerful generative form for capturing high-dimensional, non Gaussian, data. They simulta- neously perform mixture adjustment and dimensional- ity reduction. We demonstrate how such models may be advantageously aggregated by accessing mixture pa- rameters only, rather than original data. Aggregation is carried out through Bayesian estimation with a specific prior and an original variational scheme. Experimental results illustrate the effectiveness of the proposal
A Bayesian Filtering Algorithm for Gaussian Mixture Models
A Bayesian filtering algorithm is developed for a class of state-space
systems that can be modelled via Gaussian mixtures. In general, the exact
solution to this filtering problem involves an exponential growth in the number
of mixture terms and this is handled here by utilising a Gaussian mixture
reduction step after both the time and measurement updates. In addition, a
square-root implementation of the unified algorithm is presented and this
algorithm is profiled on several simulated systems. This includes the state
estimation for two non-linear systems that are strictly outside the class
considered in this paper
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