Multi-candidate missing data imputation for robust speech recognition

The application of Missing Data Techniques (MDT) to increase the noise robustness of HMM/GMM-based large vocabulary speech recognizers is hampered by a large computational burden. The likelihood evaluations imply solving many constrained least squares (CLSQ) optimization problems. As an alternative, researchers have proposed frontend MDT or have made oversimplifying independence assumptions for the backend acoustic model. In this article, we propose a fast Multi-Candidate (MC) approach that solves the per-Gaussian CLSQ problems approximately by selecting the best from a small set of candidate solutions, which are generated as the MDT solutions on a reduced set of cluster Gaussians. Experiments show that the MC MDT runs equally fast as the uncompensated recognizer while achieving the accuracy of the full backend optimization approach. The experiments also show that exploiting the more accurate acoustic model of the backend does pay off in terms of accuracy when compared to frontend MDT. © 2012 Wang and Van hamme; licensee Springer.Wang Y., Van hamme H., ''Multi-candidate missing data imputation for robust speech recognition'', EURASIP journal on audio, speech, and music processing, vol. 17, 20 pp., 2012.status: publishe

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Statistical models for noise-robust speech recognition

A standard way of improving the robustness of speech recognition systems to noise is model compensation. This replaces a speech recogniser's distributions over clean speech by ones over noise-corrupted speech. For each clean speech component, model compensation techniques usually approximate the corrupted speech distribution with a diagonal-covariance Gaussian distribution. This thesis looks into improving on this approximation in two ways: firstly, by estimating full-covariance Gaussian distributions; secondly, by approximating corrupted-speech likelihoods without any parameterised distribution. The first part of this work is about compensating for within-component feature correlations under noise. For this, the covariance matrices of the computed Gaussians should be full instead of diagonal. The estimation of off-diagonal covariance elements turns out to be sensitive to approximations. A popular approximation is the one that state-of-the-art compensation schemes, like VTS compensation, use for dynamic coefficients: the continuous-time approximation. Standard speech recognisers contain both per-time slice, static, coefficients, and dynamic coefficients, which represent signal changes over time, and are normally computed from a window of static coefficients. To remove the need for the continuous-time approximation, this thesis introduces a new technique. It first compensates a distribution over the window of statics, and then applies the same linear projection that extracts dynamic coefficients. It introduces a number of methods that address the correlation changes that occur in noise within this framework. The next problem is decoding speed with full covariances. This thesis re-analyses the previously-introduced predictive linear transformations, and shows how they can model feature correlations at low and tunable computational cost. The second part of this work removes the Gaussian assumption completely. It introduces a sampling method that, given speech and noise distributions and a mismatch function, in the limit calculates the corrupted speech likelihood exactly. For this, it transforms the integral in the likelihood expression, and then applies sequential importance resampling. Though it is too slow to use for recognition, it enables a more fine-grained assessment of compensation techniques, based on the KL divergence to the ideal compensation for one component. The KL divergence proves to predict the word error rate well. This technique also makes it possible to evaluate the impact of approximations that standard compensation schemes make.This work was supported by Toshiba Research Europe Ltd., Cambridge Research Laboratory

Métodos discriminativos para la optimización de modelos en la Verificación del Hablante

La creciente necesidad de sistemas de autenticación seguros ha motivado el interés de algoritmos efectivos de Verificación de Hablante (VH). Dicha necesidad de algoritmos de alto rendimiento, capaces de obtener tasas de error bajas, ha abierto varias ramas de investigación. En este trabajo proponemos investigar, desde un punto de vista discriminativo, un conjunto de metodologías para mejorar el desempeño del estado del arte de los sistemas de VH. En un primer enfoque investigamos la optimización de los hiper-parámetros para explícitamente considerar el compromiso entre los errores de falsa aceptación y falso rechazo. El objetivo de la optimización se puede lograr maximizando el área bajo la curva conocida como ROC (Receiver Operating Characteristic) por sus siglas en inglés. Creemos que esta optimización de los parámetros no debe de estar limitada solo a un punto de operación y una estrategia más robusta es optimizar los parámetros para incrementar el área bajo la curva, AUC (Area Under the Curve por sus siglas en inglés) de modo que todos los puntos sean maximizados. Estudiaremos cómo optimizar los parámetros utilizando la representación matemática del área bajo la curva ROC basada en la estadística de Wilcoxon Mann Whitney (WMW) y el cálculo adecuado empleando el algoritmo de descendente probabilístico generalizado. Además, analizamos el efecto y mejoras en métricas como la curva detection error tradeoff (DET), el error conocido como Equal Error Rate (EER) y el valor mínimo de la función de detección de costo, minimum value of the detection cost function (minDCF) todos ellos por sue siglas en inglés. En un segundo enfoque, investigamos la señal de voz como una combinación de atributos que contienen información del hablante, del canal y el ruido. Los sistemas de verificación convencionales entrenan modelos únicos genéricos para todos los casos, y manejan las variaciones de estos atributos ya sea usando análisis de factores o no considerando esas variaciones de manera explícita. Proponemos una nueva metodología para particionar el espacio de los datos de acuerdo a estas carcterísticas y entrenar modelos por separado para cada partición. Las particiones se pueden obtener de acuerdo a cada atributo. En esta investigación mostraremos como entrenar efectivamente los modelos de manera discriminativa para maximizar la separación entre ellos. Además, el diseño de algoritimos robustos a las condiciones de ruido juegan un papel clave que permite a los sistemas de VH operar en condiciones reales. Proponemos extender nuestras metodologías para mitigar los efectos del ruido en esas condiciones. Para nuestro primer enfoque, en una situación donde el ruido se encuentre presente, el punto de operación puede no ser solo un punto, o puede existir un corrimiento de forma impredecible. Mostraremos como nuestra metodología de maximización del área bajo la curva ROC es más robusta que la usada por clasificadores convencionales incluso cuando el ruido no está explícitamente considerado. Además, podemos encontrar ruido a diferentes relación señal a ruido (SNR) que puede degradar el desempeño del sistema. Así, es factible considerar una descomposición eficiente de las señales de voz que tome en cuenta los diferentes atributos como son SNR, el ruido y el tipo de canal. Consideramos que en lugar de abordar el problema con un modelo unificado, una descomposición en particiones del espacio de características basado en atributos especiales puede proporcionar mejores resultados. Esos atributos pueden representar diferentes canales y condiciones de ruido. Hemos analizado el potencial de estas metodologías que permiten mejorar el desempeño del estado del arte de los sistemas reduciendo el error, y por otra parte controlar los puntos de operación y mitigar los efectos del ruido

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations

Compressive sensing for signal ensembles

Compressive sensing (CS) is a new approach to simultaneous sensing and compression that enables a potentially large reduction in the sampling and computation costs for acquisition of signals having a sparse or compressible representation in some basis. The CS literature has focused almost exclusively on problems involving single signals in one or two dimensions. However, many important applications involve distributed networks or arrays of sensors. In other applications, the signal is inherently multidimensional and sensed progressively along a subset of its dimensions; examples include hyperspectral imaging and video acquisition. Initial work proposed joint sparsity models for signal ensembles that exploit both intra- and inter-signal correlation structures. Joint sparsity models enable a reduction in the total number of compressive measurements required by CS through the use of specially tailored recovery algorithms. This thesis reviews several different models for sparsity and compressibility of signal ensembles and multidimensional signals and proposes practical CS measurement schemes for these settings. For joint sparsity models, we evaluate the minimum number of measurements required under a recovery algorithm with combinatorial complexity. We also propose a framework for CS that uses a union-of-subspaces signal model. This framework leverages the structure present in certain sparse signals and can exploit both intra- and inter-signal correlations in signal ensembles. We formulate signal recovery algorithms that employ these new models to enable a reduction in the number of measurements required. Additionally, we propose the use of Kronecker product matrices as sparsity or compressibility bases for signal ensembles and multidimensional signals to jointly model all types of correlation present in the signal when each type of correlation can be expressed using sparsity. We compare the performance of standard global measurement ensembles, which act on all of the signal samples; partitioned measurements, which act on a partition of the signal with a given measurement depending only on a piece of the signal; and Kronecker product measurements, which can be implemented in distributed measurement settings. The Kronecker product formulation in the sparsity and measurement settings enables the derivation of analytical bounds for transform coding compression of signal ensembles and multidimensional signals. We also provide new theoretical results for performance of CS recovery when Kronecker product matrices are used, which in turn motivates new design criteria for distributed CS measurement schemes