Support Vector Machines for Speech Recognition

Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic modeling which can often be prone to overfitting and does not translate to improved discrimination. We propose a new paradigm centered on principles of structural risk minimization using a discriminative framework for speech recognition based on support vector machines (SVMs). SVMs have the ability to simultaneously optimize the representational and discriminative ability of the acoustic classifiers. We have developed the first SVM-based large vocabulary speech recognition system that improves performance over traditional HMM-based systems. This hybrid system achieves a state-of-the-art word error rate of 10.6% on a continuous alphadigit task ? a 10% improvement relative to an HMM system. On SWITCHBOARD, a large vocabulary task, the system improves performance over a traditional HMM system from 41.6% word error rate to 40.6%. This dissertation discusses several practical issues that arise when SVMs are incorporated into the hybrid system

Predicted protein interactions of IFITMs which inhibit Zika virus infection [version 1; referees: 1 approved, 1 approved with reservations, 1 not approved]

© 2016 Ganapathiraju MK. After the first reported case of Zika virus in Brazil, in 2015, a significant increase in the reported cases of microcephaly was observed. Microcephaly is a neurological condition in which the infant's head is significantly smaller with complications in brain development. Recently, two small membrane-associated interferon-inducible transmembrane proteins (IFITM1 and IFITM3) have been shown to repress members of the flaviviridae family which includes the Zika virus. However, the exact mechanisms leading to the inhibition of the virus are yet unknown. Here, we assembled an interactome of IFITM1 and IFITM3 with known protein-protein interactions (PPIs) collected from publicly available databases and novel PPIs predicted using High-confidence Protein-Protein Interaction Prediction (HiPPIP) model. We analyzed the functional and pathway associations of the interacting proteins, and found that there are several immunity pathways (interferon signaling, cd28 signaling in T-helper cells crosstalk between dendritic cells and natural killer cells), neuronal pathways (axonal guidance signaling, neural tube closure and actin cytoskeleton signaling) and developmental pathways that are associated with these interactors. These results could help direct future research in elucidating the mechanisms underlying the viral immunity to Zika virus and other flaviviruses

Improving recognition accuracy on CVSD speech under mismatched conditions

Emerging technology in mobile communications is seeing increasingly high acceptance as a preferred choice for last-mile communication. There have been a wide range of techniques to achieve signal compression to suit to the smaller bandwidths available on mobile communication channels; but speech recognition methods have seen success mostly only in controlled speech environments. However, designing of speech recognition systems for mobile communications is crucial in order to provide voice enabled command and control and for applications like Mobile Voice Commerce. Continuously Variable Slope Delta (CVSD) modulation, a technique for low bitrate coding of speech, has been in use particularly in military wireless environments for over 30 years, and is now also adopted by BlueTooth. CVSD is particularly suitable for Internet and mobile environments due to its robustness against transmission errors, and simplicity of implementation and the absence of a need for synchronization. In this paper, we study some characteristics of the CVSD speech in the context of robust recognition of compressed speech, and present two methods of improving the recognition accuracy in Automatic Speech Recognition (ASR) systems. We study the characteristics of the features extracted for ASR and how they relate to the corresponding features computed from Pulse Coded Modulation (PCM) speech and apply this relation to correct the CVSD features to improve recognition accuracy. Secondly we show that the ASR done on bit-streams directly, gives a good recognition accuracy and when combined with our approach gives a better accuracy

Endgame for polio eradication? Options for overcoming social and political factors in the progress to eradicating polio.

In 1988, the Global Polio Eradication Initiative (GPEI) was launched with the goal of eradicating polio by the year 2000. After 25 years, several dynamics still challenge this large public health campaign with new cases of polio being reported annually. We examine the roots of this initiative to eradicate polio, its scope, the successes and setbacks during the last 25 years and reflect on the current state of affairs. We examine the social and political factors that are barriers to polio eradication. Options are discussed for solving the current impasse of polio eradication: using force, respecting individual freedoms and gaining support from those vulnerable to fundamentalist \u27propaganda\u27. The travails of the GPEI indicate the need for expanding the Convention on the Rights of the Child to address situations of war and civic strife. Such a cultural and structural reference will provide the basis for global stakeholders to engage belligerent local actors whose local political conflicts are barriers to the eradication of polio. Disregard for these actors will result in stagnation of polio eradication policy, delaying eradication beyond 2018

Predicted protein interactions of IFITMs which inhibit Zika virus infection [version 1; referees: 1 approved, 1 approved with reservations, 1 not approved]

© 2016 Ganapathiraju MK. After the first reported case of Zika virus in Brazil, in 2015, a significant increase in the reported cases of microcephaly was observed. Microcephaly is a neurological condition in which the infant's head is significantly smaller with complications in brain development. Recently, two small membrane-associated interferon-inducible transmembrane proteins (IFITM1 and IFITM3) have been shown to repress members of the flaviviridae family which includes the Zika virus. However, the exact mechanisms leading to the inhibition of the virus are yet unknown. Here, we assembled an interactome of IFITM1 and IFITM3 with known protein-protein interactions (PPIs) collected from publicly available databases and novel PPIs predicted using High-confidence Protein-Protein Interaction Prediction (HiPPIP) model. We analyzed the functional and pathway associations of the interacting proteins, and found that there are several immunity pathways (interferon signaling, cd28 signaling in T-helper cells crosstalk between dendritic cells and natural killer cells), neuronal pathways (axonal guidance signaling, neural tube closure and actin cytoskeleton signaling) and developmental pathways that are associated with these interactors. These results could help direct future research in elucidating the mechanisms underlying the viral immunity to Zika virus and other flaviviruses

Transmembrane helix prediction using amino acid property features and latent semantic analysis

Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often restricted to the generally accepted topology "cytoplasmic-transmembrane-extracellular" and cannot adapt to membrane proteins that do not conform to this topology. Recent crystal structures of channel proteins have revealed novel architectures showing that the above topology may not be as universal as previously believed. Thus, there is a need for methods that can better predict TM helices even in novel topologies and families

LENS: Web-based lens for enrichment and network studies of human proteins

Background: Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition. Results: We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia. Conclusion: LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS