Jorge F. Silva

Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena

This paper presents the implementation of a particle-filtering-based prognostic framework that allows estimating the state of health (SOH) and predicting the remaining useful life (RUL) of energy storage devices, and more specifically lithium-ion batteries, while simultaneously detecting and isolating the effect of self-recharge phenomena within the life-cycle model. The proposed scheme and the statistical characterization of capacity regeneration phenomena are validated through experimental data from an accelerated battery degradation test and a set of ad hoc performance measures to quantify the precision and accuracy of the RUL estimates. In addition, a simplified degradation model is presented to analyze and compare the performance of the proposed approach in the case where the optimal solution (in the mean-square-error sense) can be found analytically.

Soft indexing of speech content for search in spoken documents

The paper presents the Position Specific Posterior Lattice (PSPL), a novel lossy representation of automatic speech recognition lattices that naturally lends itself to efficient indexing and subsequent relevance ranking of spoken documents. This technique explicitly takes into consideration the content uncertainty by means of using soft-hits. Indexing position information allows one to approximate N-gram expected counts and at the same time use more general proximity features in the relevance score calculation. In fact, one can easily port any state-of-the-art text-retrieval algorithm to the scenario of indexing ASR lattices for spoken documents, rather than using the 1-best recognition result. Experiments performed on a collection of lecture recordings-MIT iCampus database-show that the spoken document ranking performance was improved by 17-26% relative over the commonly used baseline of indexing the 1-best output from an automatic speech recognizer (ASR). The paper also addresses the problem of integrating speech and text content sources for the document search problem, as well as its usefulness from an ad hoc retrieval-keyword search-point of view. In this context, the PSPL formulation is naturally extended to deal with both speech and text content for a given document, where a new relevance ranking framework is proposed for integrating the different sources of information available. Experimental results on the MIT iCampus corpus show a relative improvement of 302% in Mean Average Precision (MAP) when using speech content and text-only metadata as opposed to just text-only metadata (which constitutes about 1% of the amount of data in the transcription of the speech content, measured in number of words). Further experiments show that even in scenarios for which the metadata size is artificially augmented such that it contains more than 10% of the spoken document transcription, the speech content still provides significant performance gains in MAP with respect to only using the text-metadata for relevance ranking.

Average divergence distance as a statistical discrimination measure for hidden Markov models

This paper proposes and evaluates a new statistical discrimination measure for hidden Markov models (HMMs) extending the notion of divergence, a measure of average discrimination information originally defined for two probability density functions. Similar distance measures have been proposed for the case of HMMs, but those have focused primarily on the stationary behavior of the models. However, in speech recognition applications, the transient aspects of the models have a principal role in the discrimination process and, consequently, capturing this information is crucial in the formulation of any discrimination indicator. This paper proposes the notion of average divergence distance (ADD) as a statistical discrimination measure between two HMMs, considering the transient behavior of these models. This paper provides an analytical formulation of the proposed discrimination measure, a justification of its definition based on the Viterbi decoding approach, and a formal proof that this quantity is well defined for a left-to-right HMM topology with a final nonemitting state, a standard model for basic acoustic units in automatic speech recognition (ASR) systems. Using experiments based on this discrimination measure, it is shown that ADD provides a coherent way to evaluate the discrimination dissimilarity between acoustic models.

Analysis and design of Wavelet-Packet Cepstral coefficients for automatic speech recognition

This work proposes using Wavelet-Packet Cepstral coefficients (WPPCs) as an alternative way to do filter-bank energy-based feature extraction (FE) for automatic speech recognition (ASR). The rich coverage of time-frequency properties of Wavelet Packets (WPs) is used to obtain new sets of acoustic features, in which competitive and better performances are obtained with respect to the widely adopted Mel-Frequency Cepstral coefficients (MFCCs) in the TIMIT corpus. In the analysis, concrete filter-bank design considerations are stipulated to obtain most of the phone-discriminating information embedded in the speech signal, where the filter-bank frequency selectivity, and better discrimination in the lower frequency range [200Hz-1kHz] of the acoustic spectrum are important aspects to consider.

Discriminative Wavelet Packet Filter Bank Selection for Pattern Recognition

This paper addresses the problem of discriminative wavelet packet (WP) filter bank selection for pattern recognition. The problem is formulated as a complexity regularized optimization criterion, where the tree-indexed structure of the WP bases is explored to find conditions for reducing this criterion to a type of minimum cost tree pruning, a method well understood in regression and classification trees (CART). For estimating the conditional mutual information, adopted to compute the fidelity criterion of the minimum cost tree-pruning problem, a nonparametric approach based on product adaptive partitions is proposed, extending the Darbellay-Vajda data-dependent partition algorithm. Finally, experimental evaluation within an automatic speech recognition (ASR) task shows that proposed solutions for the WP decomposition problem are consistent with well understood empirically determined acoustic features, and the derived feature representations yield competitive performances with respect to standard feature extraction techniques.

Nonproduct Data-Dependent Partitions for Mutual Information Estimation: Strong Consistency and Applications

A new framework for histogram-based mutual information estimation of probability distributions equipped with density functions in (Rd,B(Rd)) is presented in this work. A general histogram-based estimate is proposed, considering nonproduct data-dependent partitions, and sufficient conditions are stipulated to guarantee a strongly consistent estimate for mutual information. Two emblematic families of density-free strongly consistent estimates are derived from this result, one based on statistically equivalent blocks (the Gessamans partition) and the other, on a tree-structured vector quantization scheme.

Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles

We present the implementation of a particle-filtering-based prognostic framework that utilizes statistical characterization of use profiles to (i) estimate the state-of-charge (SOC), and (ii) predict the discharge time of energy storage devices (lithium-ion batteries). The proposed approach uses a novel empirical state-space model, inspired by battery phenomenology, and particle-filtering algorithms to estimate SOC and other unknown model parameters in real-time. The adaptation mechanism used during the filtering stage improves the convergence of the state estimate, and provides adequate initial conditions for the prognosis stage. SOC prognosis is implemented using a particle-filtering-based framework that considers a statistical characterization of uncertainty for future discharge profiles based on maximum likelihood estimates of transition probabilities for a two-state Markov chain. All algorithms have been trained and validated using experimental data acquired from one Li-Ion 26650 and two Li-Ion 18650 cells, and considering different operating conditions.

Universal Consistency of Data-Driven Partitions for Divergence Estimation

This paper presents a general histogram based divergence estimator based on data-dependent partition. Sufficient conditions for the universal strong consistency of the data-driven divergence estimator, using Lugosi and Nobels combinatorial notions for partition families, are presented. As a corollary this result is particularized for the emblematic case of l m-spacing quantization scheme.

Upper Bound Kullback–Leibler Divergence for Transient Hidden Markov Models

This paper reports an upper bound for the Kullback-Leibler divergence (KLD) for a general family of transient hidden Markov models (HMMs). An upper bound KLD (UBKLD) expression for Gaussian mixtures models (GMMs) is presented which is generalized for the case of HMMs. Moreover, this formulation is extended to the case of HMMs with nonemitting states, where under some general assumptions, the UBKLD is proved to be well defined for a general family of transient models. In particular, the UBKLD has a computationally efficient closed-form for HMMs with left-to-right topology and a final nonemitting state, that we refer to as left-to-right transient HMMs. Finally, the usefulness of the closed-form expression is experimentally evaluated for automatic speech recognition (ASR) applications, where left-to-right transient HMMs are used to model basic acoustic-phonetic units. Results show that the UBKLD is an accurate discrimination indicator for comparing acoustic HMMs used for ASR.

Analysis and interpretation of the Cramer-Rao lower-bound in astrometry: One dimensional case

ABSTRACT.In this article we explore the maximum precision attainable in the location of a point source imaged by a pixel array detector in the presence of a background, as a function of the detector properties. For this we use a well-known result from parametric estimation theory, the so-called Cramer-Rao lower bound. We develop the expressions in the one-dimensional case of a linear array detector in which the only unknown parameter is the source position. If the object is oversampled by the detector, analytical expressions can be obtained for the Cramer-Rao limit that can be readily used to estimate the limiting precision of an imaging system, and which are very useful for experimental (detector) design, observational planning, or performance estimation of data analysis software: In particular, we demonstrate that for background-dominated sources, the maximum astrometric precision goes as B/F2B/F2, where BB is the background in one pixel, and FF is the total flux of the source, while when the background...