This thesis presents a method to investigate the extent to which articulatory based acoustic features can be exploited to reduce ambiguity in automatic speech recognition search. The method proposed is based on a lattice re-scoring paradigm implemented to integrate articulatory based features into automatic speech recognition systems. Time delay neural networks are trained as feature detectors to generate feature streams over which hidden Markov models (HMMs) are defined. These articulatory based HMMs are combined with HMMs defined over spectral energy based Mel frequency cepstrum coefficient (MFCC) acoustic features through a sequential lattice re-scoring procedure. The optimum phone strings are found by maximizing the log-linear combination of acoustic and language models likelihoods during recognition. The associated log-linear weights are estimated using a discriminative model combination approach. All the experiments are performed using the DARPA TIMIT speech database and the results are presented in terms of phone accuracies.

Thesis (Ph. D.)--University of Wyoming, 2005. / Title from PDF title page (viewed on March 10, 2008). Includes bibliographical references (p. 107-111).

Thesis (M.Eng.(Mechanical Engineering))--University of Pretoria, 2004. / Summaries in Afrikaans and English. Includes bibliographical references (leaves 80-82).

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2010. / Thesis Advisor(s): Kress, Moshe ; Szechtman, Roberto ; Second Reader: Atkinson, Michael. "June 2010." Description based on title screen as viewed on July 14, 2010. Author(s) subject terms: Lebanese Hizbullah; Lebanese Diaspora; Lebanon; Markov Decision Process; Dynamic Bayesian Network; Hidden Markov Models; Decision Analysis; Decision Theory; Decision Tree; State Tree; Influence Diagram; GeNIe; Stakeholder; State Space; Rational Actor; Action; Interest; Distribution; Forecast. Includes bibliographical references (p. 65). Also available in print.

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Wireless has proved to be one of the most important and fastest growing fields of communications especially during last few decades. To achieve reliable communication, we model a wireless system to analyze its performance and to find ways to improve the reliability of a particular system. Extensive research is being done to accurately model wireless systems, and to achieve better performance. Simulation techniques have been in use for many years to support the design and evaluation of electronic communication systems. Over the past few decades, Computer Aided Design (CAD) techniques (including both computerized analytical techniques and simulation) have matured, and are now usually applied at some point in the system design/development process. The aim of this thesis is to find efficient algorithms that can model third generation wireless channels in a discrete sense. For modeling these channels, mathematical tools known as hidden Markov models are used. These models have proved themselves to be very efficient in many areas of electrical engineering including speech recognition, pattern recognition, artificial intelligence, wavelets and queuing theory. Wideband Code Division Multiple Access (W-CDMA) wireless communication parameters including channels fading statistics, Bit Error Rate (BER) performance and interval distribution of errors are modeled using different Markov models, and their results are tested and validated. Four algorithms for modeling error sources are implemented, and their results are discussed. Both hidden Markov models and semi-hidden Markov models are used in this thesis, and their results are validated for the W-CDMA environment. The state duration distributions for these channels are also approximated using Phase-Type (PH) distribution. / Master of Science

Methods to detect patterns of variation associated with ongoing positive selection often focus on identifying regions of the genome with extended haplotype homozygosity - indicative of recently shared ancestry. Whilst these have been shown to be powerful they have two major challenges. First, these methods are constructed to detect variation associated with a classical selective sweep; a single haplotype background gets swept up to a higher than expected frequency given its age. Recently studies have shown that other forms of positive selection, e.g. selection on standing variation, may be more prevalent than previous thought. Under such evolution, a mutation that is already segregating in the population becomes beneficial, possibly as a result of an environmental change. The second challenge with these methods is that they base their inference on non-parametric tests of significance which can result in uncontrolled false positive rates. We tackle these problems using two approaches. First, by exploiting a widely used model in population genomics we construct a new approach to detect regions where a subset of the chromosomes are much more related than expected genome-wide. Using this metric we show that it is sensitive to both classical selective sweeps, and to soft selective sweeps, e.g. selection on standing variation. Second, building on existing methods, we construct a Bayesian test which bi-partitions chromosomes at every position based on their allelic type and tests for association between chromosomes carrying one allele and significantly reduced time to common ancestor. Using simulated data we show that this approach results in a powerful, fast, and robust approach to detect signals of positive selection in sequence data. Moreover by comparing our model to existing techniques we show that we have similar power to detect recent classical selective sweeps, and considerably greater power to detect soft selective sweeps. We apply our method, ABACUS, to three human populations using data from the 1000 Genome Project. Using existing and novel candidates of positive selection, we show that the results between ABACUS and existing methods are comparable in regions of classical selection, and are arguably superior in regions that show evidence for recent selection on standing variation.

For many sequence prediction tasks in Natural Language Processing, modeling dependencies between individual predictions can be used to improve prediction accuracy of the sequence as a whole. Supertagging, involves assigning lexical entries to words based on lexicalized grammatical theory such as Combinatory Categorial Grammar (CCG). Previous work has used Bayesian HMMs to learn taggers for both POS tagging and supertagging separately. Modeling them jointly has the potential to produce more robust and accurate supertaggers trained with less supervision and thereby potentially help in the creation of useful models for new languages and domains. Factorial Hidden Markov Models (FHMM) support joint inference for multiple sequence prediction tasks. Here, I use them to jointly predict part-of-speech tag and supertag sequences with varying levels of supervision. I show that supervised training of FHMM models improves performance compared to standard HMMs, especially when labeled training material is scarce. Secondly, FHMMs trained from tag dictionaries rather than labeled examples also perform better than a standard HMM. Finally, I show that an FHMM and a maximum entropy Markov model can complement each other in a single step co-training setup that improves the performance of both models when there is limited labeled training material available. / text

A Profile Hidden Markov Model (HMM) is a statistical model for representing a multiple sequence alignment (MSA). Profile HMMs are important tools for sequence homology detection and have been used in wide a range of bioinformatics applications including protein structure prediction, remote homology detection, and sequence alignment. Profile HMM methods result in accurate alignments on datasets with evolutionarily similar sequences; however, I will show that on datasets with evolutionarily divergent sequences, the accuracy of HMM-based methods degrade. My dissertation presents a new statistical model for representing an MSA by using a set of HMMs. The family of HMM (fHMM) approach uses multiple HMMs instead of a single HMM to represent an MSA. I present a new algorithm for sequence alignment using the fHMM technique. I show that using the fHMM technique for sequence alignment results in more accurate alignments than the single HMM approach. As sequence alignment is a fundamental step in many bioinformatics pipelines, improvements to sequence alignment result in improvements across many different fields. I show the applicability of fHMM to three specific problems: phylogenetic placement, taxonomic profiling and identification, and MSA estimation. In phylogenetic placement, the problem addressed is how to insert a query sequence into an existing tree. In taxonomic identification and profiling, the problems addressed are how to taxonomically classify a query sequence, and how to estimate a taxonomic profile on a set of sequences. Finally, both profile HMM and fHMM require a backbone MSA as input in order to align the query sequences. In MSA estimation, the problem addressed is how to estimate a ``de novo'' MSA without the use of an existing backbone alignment. For each problem, I present a software pipeline that implements the fHMM specifically for that domain: SEPP for phylogenetic placement, TIPP for taxonomic profiling and identification, and UPP for MSA estimation. I show that SEPP has improved accuracy compared to the single HMM approach. I also show that SEPP results in more accurate phylogenetic placements compared to existing placement methods, and SEPP is more computationally efficient, both in peak memory usage and running time. I show that TIPP more accurately classifies novel sequences compared to the single HMM approach, and TIPP estimates more accurate taxonomic profiles than leading methods on simulated metagenomic datasets. I show how UPP can estimate ``de novo'' alignments using fHMM. I present results that show UPP is more accurate and efficient than existing alignment methods, and estimates accurate alignments and trees on datasets containing both full-length and fragmentary sequences. Finally, I show that UPP can estimate a very accurate alignment on a dataset with 1,000,000 sequences in less than 2 days without the need of a supercomputer. / Computer Sciences / text