Approaching and Executing Premiere, Interpretive, and Historical Works

The spectrum of vocal music spans time, genres, styles, and is infinitely vast. New works are ever evolving and expanding, new artistic ideas are revealed from older works, and interest renewed from the tried and true. As a vocal musician in present day, I aspired to find a common thread amidst the boundless spectrum of works to be performed—whether I was hearkening back to a time of old, dissecting pieces by composers who have opened the door to personal artistry, or learning to sing a new work never performed or heard before. The Mercuriality of Song unearths more differences than commonalities in preparation, despite the fact that my voice remains the constant— differences which were expected, often surprising, but nevertheless new and rewarding in their challenges. Three performances (a world-premiere, a lieder recital, and an early music recital) comprise the basis for my investigation into comparing methods and processes of different periods via program notes, laying the foundation for initial preparation from an historical context. An amalgam of genres and stylistic differences along with performance planning culminate this exploration of vocal discovery and implementation.

CROSS-LINGUISTIC TRANSFER OF SPELLING SKILLS IN SPANISH-SPEAKING ADULT ESL LEARNERS

Spelling is an important literacy skill, and learning to spell is an important component of learning to write. Learners with strong spelling skills also exhibit greater reading, vocabulary, and orthographic knowledge than those with poor spelling skills (Ehri & Rosenthal, 2007; Ehri & Wilce, 1987; Rankin, Bruning, Timme, & Katkanant, 1993). English, being a deep orthography, has inconsistent sound-to-letter correspondences (Seymour, 2005; Ziegler & Goswami, 2005). This poses a great challenge for learners in gaining spelling fluency and accuracy. The purpose of the present study is to examine cross-linguistic transfer of English vowel spellings in Spanish-speaking adult ESL learners. The research participants were 129 Spanish-speaking adult ESL learners and 104 native English-speaking GED students enrolled in a community college located in the South Atlantic region of the United States. The adult ESL participants were in classes at three different levels of English proficiency: advanced, intermediate, and beginning. An experimental English spelling test was administered to both the native English-speaking and ESL participants. In addition, the adult ESL participants took the standardized spelling tests to rank their spelling skills in both English and Spanish. The data were analyzed using robust regression and Poisson regression procedures, Mann-Whitney test, and descriptive statistics. The study found that both Spanish spelling skills and English proficiency are strong predictors of English spelling skills. Spanish spelling is also a strong predictor of level of L1-influenced transfer. More proficient Spanish spellers made significantly fewer L1-influenced spelling errors than less proficient Spanish spellers. L1-influenced transfer of spelling knowledge from Spanish to English likely occurred in three vowel targets (/ɑɪ/ spelled as ae, ai, or ay, /ɑʊ/ spelled as au, and /eɪ/ spelled as e). The ESL participants and the native English-speaking participants produced highly similar error patterns of English vowel spellings when the errors did not indicate L1-influenced transfer, which implies that the two groups might follow similar trajectories of developing English spelling skills. The findings may help guide future researchers or practitioners to modify and develop instructional spelling intervention to meet the needs of adult ESL learners and help them gain English spelling competence.

W.A. Mozart's Die Zauberflöte An Arrangement and Performance Edition for Wind Instruments and Vocal Soloists

The purpose of this dissertation is to produce a new Harmonie arrangement of Mozart’s Die Zauberflöte suitable for modern performance, bringing Joseph Heidenreich’s 1782 arrangement—one of the great treasures of the wind repertoire—to life for future performers and audiences. I took advantage of the capabilities of modern wind instruments and performance techniques, and employed other instruments normally found in the modern wind ensemble to create a work in the tradition of Heidenreich’s that restored as much of Mozart’s original thinking as possible. I expanded the Harmonie band to include flute and string bass. Other instruments provide special effects, a traditional role for wind instruments in the Classical opera orchestra. This arrangement is conceived to be performed with the original vocal soloists, making it a viable option for concert performance or for smaller staged productions. It is also intended to allow the wind players to be onstage with the singers, becoming part of the dramatic action while simultaneously serving as the “opera orchestra.” This allows creative staging possibilities, and offers the wind players an opportunity to explore new aspects of performing. My arrangement also restores Mozart’s music to its original keys and retains much of his original wind scoring. This arrangement expands the possibilities for collaboration between opera studios, voice departments or community opera companies and wind ensembles. A suite for winds without voices (currently in production) will allow conductors to program this major work from the Classical era without dedicating a concert program to the complete opera. Excerpted arias and duets from this arrangement provide vocalists the option of using chamber wind accompaniment on recitals. The door is now open to arrangements of other operas by composers such as Mozart, Rossini and Weber, adding new repertoire for chamber winds and bringing great music to life in a new way.

Easy PRAM-based High-performance Parallel Programming with ICE

A poster of this paper will be presented at the 25th International Conference on Parallel Architecture and Compilation Technology (PACT ’16), September 11-15, 2016, Haifa, Israel.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.