A Subband-Based SVM Front-End for Robust ASR

This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using ensemble methods are addressed. The proposed front-end is compared with state-of-the-art ASR front-ends in terms of robustness to additive noise and linear filtering. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of the proposed subband based SVM front-end: it outperforms the standard cepstral front-end in the presence of noise and linear filtering for signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed front-end with a conventional front-end such as MFCC yields further improvements over the individual front ends across the full range of noise levels

Modern Coding Theory: The Statistical Mechanics and Computer Science Point of View

These are the notes for a set of lectures delivered by the two authors at the Les Houches Summer School on `Complex Systems' in July 2006. They provide an introduction to the basic concepts in modern (probabilistic) coding theory, highlighting connections with statistical mechanics. We also stress common concepts with other disciplines dealing with similar problems that can be generically referred to as `large graphical models'. While most of the lectures are devoted to the classical channel coding problem over simple memoryless channels, we present a discussion of more complex channel models. We conclude with an overview of the main open challenges in the field.Comment: Lectures at Les Houches Summer School on `Complex Systems', July 2006, 44 pages, 25 ps figure

Contextual Bag-Of-Visual-Words and ECOC-Rank for Retrieval and Multi-class Object Recognition

Projecte Final de Màster UPC realitzat en col.laboració amb Dept. Matemàtica Aplicada i Anàlisi, Universitat de BarcelonaMulti-class object categorization is an important line of research in Computer Vision and Pattern Recognition fields. An artificial intelligent system is able to interact with its environment if it is able to distinguish among a set of cases, instances, situations, objects, etc. The World is inherently multi-class, and thus, the eficiency of a system can be determined by its accuracy discriminating among a set of cases. A recently applied procedure in the literature is the Bag-Of-Visual-Words (BOVW). This methodology is based on the natural language processing theory, where a set of sentences are defined based on word frequencies. Analogy, in the pattern recognition domain, an object is described based on the frequency of its parts appearance. However, a general drawback of this method is that the dictionary construction does not take into account geometrical information about object parts. In order to include parts relations in the BOVW model, we propose the Contextual BOVW (C-BOVW), where the dictionary construction is guided by a geometricaly-based merging procedure. As a result, objects are described as sentences where geometrical information is implicitly considered. In order to extend the proposed system to the multi-class case, we used the Error-Correcting Output Codes framework (ECOC). State-of-the-art multi-class techniques are frequently defined as an ensemble of binary classifiers. In this sense, the ECOC framework, based on error-correcting principles, showed to be a powerful tool, being able to classify a huge number of classes at the same time that corrects classification errors produced by the individual learners. In our case, the C-BOVW sentences are learnt by means of an ECOC configuration, obtaining high discriminative power. Moreover, we used the ECOC outputs obtained by the new methodology to rank classes. In some situations, more than one label is required to work with multiple hypothesis and find similar cases, such as in the well-known retrieval problems. In this sense, we also included contextual and semantic information to modify the ECOC outputs and defined an ECOC-rank methodology. Altering the ECOC output values by means of the adjacency of classes based on features and classes relations based on ontologies, we also reporteda significant improvement in class-retrieval problems