Time–Frequency Cepstral Features and Heteroscedastic Linear Discriminant Analysis for Language Recognition

The shifted delta cepstrum (SDC) is a widely used feature extraction for language recognition (LRE). With a high context width due to incorporation of multiple frames, SDC outperforms traditional delta and acceleration feature vectors. However, it also introduces correlation into the concatenated feature vector, which increases redundancy and may degrade the performance of backend classifiers. In this paper, we first propose a time-frequency cepstral (TFC) feature vector, which is obtained by performing a temporal discrete cosine transform (DCT) on the cepstrum matrix and selecting the transformed elements in a zigzag scan order. Beyond this, we increase discriminability through a heteroscedastic linear discriminant analysis (HLDA) on the full cepstrum matrix. By utilizing block diagonal matrix constraints, the large HLDA problem is then reduced to several smaller HLDA problems, creating a block diagonal HLDA (BDHLDA) algorithm which has much lower computational complexity. The BDHLDA method is finally extended to the GMM domain, using the simpler TFC features during re-estimation to provide significantly improved computation speed. Experiments on NIST 2003 and 2007 LRE evaluation corpora show that TFC is more effective than SDC, and that the GMM-based BDHLDA results in lower equal error rate (EER) and minimum average cost (Cavg) than either TFC or SDC approaches

Linear classifier design under heteroscedasticity in Linear Discriminant Analysis

Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification. In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution. We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers. The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity. While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets. Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets. Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems

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

Discriminant feature analysis for pattern recognition

Ph.DDOCTOR OF PHILOSOPH

LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

Tracing the assembly history of NGC 1395 through its Globular Cluster System

We used deep Gemini-South/GMOS g'r'i'z' images to study the globular cluster (GC) system of the massive elliptical galaxy NGC1395, located in the Eridanus supergroup. The photometric analysis of the GC candidates reveals a clear colour bimodality distribution, indicating the presence of 'blue' and 'red' GC subpopulations. While a negative radial colour gradient is detected in the projected spatial distribution of the red GCs, the blue GCs display a shallow colour gradient. The blue GCs also display a remarkable shallow and extended surface density profile, suggesting a significant accretion of low-mass satellites in the outer halo of the galaxy. In addition, the slope of the projected spatial distribution of the blue GCs in the outer regions of the galaxy, is similar to that of the X-ray halo emission. Integrating up to 165 kpc the profile of the projected spatial distribution of the GCs, we estimated a total GC population and specific frequency of 6000 ± 1100 and S N = 7.4 ± 1.4, respectively. Regarding NGC1395 itself, the analysis of the deep Gemini/GMOS images shows a low surface brightness umbrella-like structure indicating, at least, one recent merger event. Through relations recently published in the literature, we obtained global parameters, such as M stellar = 9.32 × 10 11 M⊙ and M h = 6.46 × 10 13 M⊙. Using public spectroscopic data, we derive stellar population parameters of the central region of the galaxy by the full spectral fitting technique. We have found that this region seems to be dominated for an old stellar population, in contrast to findings of young stellar populations from the literature.Instituto de Astrofísica de La PlataFacultad de Ciencias Astronómicas y Geofísica

Tracing the assembly history of NGC 1395 through its Globular Cluster System

We used deep Gemini-South/GMOS g'r'i'z' images to study the globular cluster (GC) system of the massive elliptical galaxy NGC1395, located in the Eridanus supergroup. The photometric analysis of the GC candidates reveals a clear colour bimodality distribution, indicating the presence of 'blue' and 'red' GC subpopulations. While a negative radial colour gradient is detected in the projected spatial distribution of the red GCs, the blue GCs display a shallow colour gradient. The blue GCs also display a remarkable shallow and extended surface density profile, suggesting a significant accretion of low-mass satellites in the outer halo of the galaxy. In addition, the slope of the projected spatial distribution of the blue GCs in the outer regions of the galaxy, is similar to that of the X-ray halo emission. Integrating up to 165 kpc the profile of the projected spatial distribution of the GCs, we estimated a total GC population and specific frequency of 6000 ± 1100 and S N = 7.4 ± 1.4, respectively. Regarding NGC1395 itself, the analysis of the deep Gemini/GMOS images shows a low surface brightness umbrella-like structure indicating, at least, one recent merger event. Through relations recently published in the literature, we obtained global parameters, such as M stellar = 9.32 × 10 11 M⊙ and M h = 6.46 × 10 13 M⊙. Using public spectroscopic data, we derive stellar population parameters of the central region of the galaxy by the full spectral fitting technique. We have found that this region seems to be dominated for an old stellar population, in contrast to findings of young stellar populations from the literature.Instituto de Astrofísica de La PlataFacultad de Ciencias Astronómicas y Geofísica

Adapting Quantitative Protein and Phosphorylation Analyses to a Proteome-Wide Scale

Liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) has become the preferred method for large-scale peptide and phosphopeptide identification and quantification. The dominance of LC-MS/MS is the result of improved chromatographic, mass spectrometry and bioinformatic technologies. The applications of these technological improvements drive biological innovation by expanding the realm of possible experimentation, facilitating the creation and evaluation of novel hypotheses. Such improvements are the focus of this dissertation. New technologies are presented and their proteome wide applications in biological systems are demonstrated. A comparison of common phosphopeptide enrichment methods is presented in chapter two, which demonstrates that a combination of methods provides non-overlapping data sets. This comparison was performed in mitotically arrested fission yeast, a previously unstudied system by phosphoproteomic methods. This chapter remarks upon phosphorylation site conservation between lower and higher eukaryotes, as a means of predicting potentially relevant phosphorylation events in mammals. A new protocol for tissue based peptide quantification is presented in chapter three. The large-scale application of this method is detailed in a system of mouse liver phosphorylation, between fasted and re-fed states. The effect of peptide and protein level false discovery rates on the accuracy of phosphorylation site quantification is highlighted. This method is a cost-effective alternative to available techniques, such as metabolic labeling, and expands the application of proteomics to include larger animals. Finally, an in depth analysis of quantitative LC-MS/MS based multiplexing is the subject of the last chapter. New techniques for peptide pre-fractionation and ion quantification are discussed, which improve proteome coverage and quantitative accuracy. This proteome-wide multiplexing is applied to an analysis of the budding yeast environmental stress response. Applicable methods of data processing and a means of obtaining biologically relevant information out of multidimensional proteomic data sets are discussed. In all chapters, the data presented represent the largest analyses of their kind. This dissertation provides a solid guide for future proteome-wide studies, focused on the identification and quantification of peptides and their posttranslational modifications

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans