The Acquisition of Vowel Normalization during Early Infancy: Theory and Computational Framework

Vowel normalization is a computation that is meant to account for the differences in the absolute direct (physical or psychophysical) representations of qualitatively equivalent vowel productions that arise due to differences in speaker properties such as body size types, age, gender, and other socially interpreted categories that are based on natural variation in vocal tract size and shape. In this dissertation, we address the metaphysical and epistemological aspects of vowel normalization pertaining to spoken language acquisition during early infancy. We begin by reviewing approaches to conceptualizing and modeling the phonetic components of early spoken language acquisition, forming a catalog of phenomena that serves as the basis for our discourse. We then establish the existence of a vowel normalization computation carried out by infants early in their spoken language acquisition, and put forward a conceptual and technical framework for its investigation which focuses attention on the generative nature of the computation. We then situate the acquisition of vowel normalization within a broader developmental framework encompassing a suite of vocal learning phenomena, including language-specific caretaker vocal exchanges, perceptual warping, and multisensory matching and narrowing. We demonstrate the applicability of the technical formulation through the creation of a virtual environment for vocal learning which provides the means to model the acquisition of vowel normalization, along with other aspects of vocal learning. We conclude with a discussion of the broader implications of the conceptual and technical formulation. ii ACKNOWLEDGMENTS Beginning with the principal contributors, I would like to thank Mary Beckman for providing the motivation and many of the subtle mechanisms for unlearning the host of epistemological prejudices that I brought with me into linguistics from mathematics, particularly with respect to the nature of progress in a developing science such as ours. I would like to thank Eric Fosler-Lussier for shaping the hard structural component of this odd endeavor, without which I would have no means to even attempt to demonstrate the merit of what I have put forward in this dissertation. And I would like to thank Carl Pollard, who right from the beginning of my time in the program served as a clear example of how to tenaciously seek alternatives to linguistic dogma. As openness and entrepreneurship are far more important than intelligence, computing prowess, analytic ability, or any of the cruder “tagline” and “showmanship” characteristics prized in academia (and especially linguistics) these days, it is difficult to overstate the value of these contributions. Indeed, this collaborative creation of an open scientific mind is very likely the only significant achievement associated with this dissertation, and surely the only one that will mean anything to anyone as time goes by. The contributors, of course, extend well beyond the principal group. I would like to thank William Schuler for bringing his expertise in computational modeling to bear on the later stages of the dissertation project, and for his helpful comments and genuine interest in the topic. I would also like to thank Misha Belkin for bringing the manifold learning iii approach to my attention, and for his advice on how best to make use of it in modeling the acquisition of vowel normalization. I would like to thank Ilana Heintz for being a welcoming and encouraging student mentor within the HSD research group. The work in this dissertation is in many ways an extension of the research that she spearheaded. I would also like to thank Pat Reidy and Jeff Holliday, not only for their camaraderie over the years, but also their efforts to implement the auditory models used in this dissertation, and investigate their role in acquisition. Beyond those at Ohio State, I would like to thank the group of researchers who carried out the perceptual categorization experiments that made the work in this dissertation possible, including Ben Munson, Catherine McBride-Chang, Chanelle Mays, Asimina Syrika, Kiyoko Yoneyama, and Hyunju Chung. I am also grateful to Ben for his expert readiness in vouching for me as a hireable researcher. The stimuli for the aforementioned experiments were provided by Lucie Ménard, whom I would like to thank, along with Louis-Jean Boë, for providing me with the matlab version of the Vlab articulatory model. This dissertation would not have been possible without the OSU linguistics faculty, staff, and students. I would like to thank Brian Joseph and Chris Brew for their encouragement and willingness to engage with me on many issues ranging over the long history and general nature of linguistics and its practice. I am also very grateful to the entire faculty for providing me with guidance and instruction during my time in the department. I would like to thank Jim Harmon for all of his hard work maintaining the linguistics computing infrastructure. I am very grateful to Julie McGory, Claudia Morettini, Julia Papke, and the undergraduate interns for their supply of clues that led me through various bureaucratic labyrinths. I am also very grateful to Hope Dawson for helping me through teaching in the iv final year of dissertation writing. I would like to thank my Oxley mates Dennis Mehay, Dominic Espinosa, Raja Rajkumar, Steve Boxwell, Kirk Baker, Jianguo Li, D. J. Hovermale, Murat Yasavul, Rory Turnbull, and Liz McCullough for providing the human resources necessary to make coming into the office a good experience. I am very grateful to the entire student body for creating a pleasant working environment. I am also grateful to the students for putting together countless cordial social engagements, providing much needed distraction from the difficulties of the program. Specifically, I would like to thank Dominic Espinosa and his family, and Abby Walker and her extended group of friends, for creating opportunities to get away from the department and linguistics issues for a time. I would also like to thank the LBS for many nights of carousing around Columbus. Finally, I would like to thank those closest to me. I am very grateful to my parents, Jude and Patricia, my sisters Tanya, Elizabeth, and Kate, my brothers-in-law Glenn and David, my niece Emily and my nephew Matthew, for providing the basis of strength and examples of perseverance I needed to keep moving forward. I am also grateful to my entire family for their love and support on my long and complicated journey out into the world. And I would like to thank Marivic Lesho, along with Hatchet and Seaweed and the faculty of Snugglemore College, for much needed love, support, and encouragement, especially during the rather difficult last few years of graduate school. This research was supported in part by NSF grants BSC-0729306, BSC-0729277, and BSC-0729140 to Mary Beckman, Eric Fosler-Lussier, Benjamin Munson, and Jan Edwards for “DHB/Collaborative Research: Using Machine Learning to Model the Interplay of Production Dynamics and Perception Dynamics in Phonological Acquisition”, and by an Ohio State University Center for Cognitive Science seed grant to Mikhail Belkin, Mary v Beckman, and Eric Fosler-Lussier for research on “Using manifolds to model phonological learning in infancy and early childhood”.