256 research outputs found
Emotion recognition from speech: tools and challenges
Human emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on one hand and about where the emotion related information lies in the speech signal on the other side. These diversities motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different emotions in the same speech portion especially in the non-prompted data sets, which tends to be more “natural” than the acted datasets where the subjects attempt to suppress all but one emotion. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only
Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification
This paper considers the classification of linear subspaces with mismatched
classifiers. In particular, we assume a model where one observes signals in the
presence of isotropic Gaussian noise and the distribution of the signals
conditioned on a given class is Gaussian with a zero mean and a low-rank
covariance matrix. We also assume that the classifier knows only a mismatched
version of the parameters of input distribution in lieu of the true parameters.
By constructing an asymptotic low-noise expansion of an upper bound to the
error probability of such a mismatched classifier, we provide sufficient
conditions for reliable classification in the low-noise regime that are able to
sharply predict the absence of a classification error floor. Such conditions
are a function of the geometry of the true signal distribution, the geometry of
the mismatched signal distributions as well as the interplay between such
geometries, namely, the principal angles and the overlap between the true and
the mismatched signal subspaces. Numerical results demonstrate that our
conditions for reliable classification can sharply predict the behavior of a
mismatched classifier both with synthetic data and in a motion segmentation and
a hand-written digit classification applications.Comment: 17 pages, 7 figures, submitted to IEEE Transactions on Signal
Processin
Sequential Gradient Coding For Straggler Mitigation
In distributed computing, slower nodes (stragglers) usually become a
bottleneck. Gradient Coding (GC), introduced by Tandon et al., is an efficient
technique that uses principles of error-correcting codes to distribute gradient
computation in the presence of stragglers. In this paper, we consider the
distributed computation of a sequence of gradients ,
where processing of each gradient starts in round- and finishes by
round-. Here denotes a delay parameter. For the GC scheme,
coding is only across computing nodes and this results in a solution where
. On the other hand, having allows for designing schemes which
exploit the temporal dimension as well. In this work, we propose two schemes
that demonstrate improved performance compared to GC. Our first scheme combines
GC with selective repetition of previously unfinished tasks and achieves
improved straggler mitigation. In our second scheme, which constitutes our main
contribution, we apply GC to a subset of the tasks and repetition for the
remainder of the tasks. We then multiplex these two classes of tasks across
workers and rounds in an adaptive manner, based on past straggler patterns.
Using theoretical analysis, we demonstrate that our second scheme achieves
significant reduction in the computational load. In our experiments, we study a
practical setting of concurrently training multiple neural networks over an AWS
Lambda cluster involving 256 worker nodes, where our framework naturally
applies. We demonstrate that the latter scheme can yield a 16\% improvement in
runtime over the baseline GC scheme, in the presence of naturally occurring,
non-simulated stragglers
Graph-based Estimation of Information Divergence Functions
abstract: Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is rarely the case in real-word scenarios. Non-parametric density estimators are characterized by a very large number of parameters that have to be tuned with costly cross-validation. In this dissertation we focus on a specific family of non-parametric estimators, called direct estimators, that bypass density estimation completely and directly estimate the quantity of interest from the data. We introduce a new divergence measure, the -divergence, that can be estimated directly from samples without parametric assumptions on the distribution. We show that the -divergence bounds the binary, cross-domain, and multi-class Bayes error rates and, in certain cases, provides provably tighter bounds than the Hellinger divergence. In addition, we also propose a new methodology that allows the experimenter to construct direct estimators for existing divergence measures or to construct new divergence measures with custom properties that are tailored to the application. To examine the practical efficacy of these new methods, we evaluate them in a statistical learning framework on a series of real-world data science problems involving speech-based monitoring of neuro-motor disorders.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
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