Phonetics and phonology are the sciences that study human speech sounds and sound patterns. At Maryland we take this to mean building mental models for speech sounds and sound patterns.
Phonetics is traditionally divided into three areas: articulatory phonetics (how speech sounds are produced by the tongue and mouth), acoustic phonetics (the physical properties of the resulting sound waves) and auditory phonetics (how speech sounds are processed and perceived by the ear and brain). Our main research emphasis is on speech perception which connects with the strong auditory neuroscience community at the Maryland Center for the Comparative and Evolutionary Biology of Hearing. Phonology adds to these areas by also studying the representation of sounds and words in long-term memory.
Phonological studies at Maryland have strong connections to the other areas of the department. We study how people (and machines) can perceive speech and recognize words using computational, psycholinguistic and neurolinguistic methods. We are fortunate to have our own magneto-encephalographic (MEG) system, which allows us to record ongoing brain activity to try to discover the code that the brain uses to represent speech. Interestingly, the brain seems to use both location in the auditory cortex and timing patterns to represent various properties of speech sounds. We also study how children (and machines) can learn the sounds and sound patterns of their native languages. Many of these studies are done in conjunction with the Infant Language Lab. In these studies young children (some as young as two months) match spoken words with faces or detect changes in sound patterns. We also employ computational models of sound pattern learning and word learning, especially non-parametric Bayesian methods to discover speech sound categories, such as vowel systems.
We make a strong attempt at Maryland to "close the circle" on phonological problems. That is, we seek models of speech sounds and patterns that can be rigorously formulated computationally and that make specific predictions which can be tested with psycholinguistic and neurolinguistic methods. Researchers are encouraged to work together to solve these problems from problem statement to algorithm to brain implementation.
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There is a simplicity bias when generalising from ambiguous data
How do phonological learners choose among generalizations of differing complexity?
How exactly do learners generalize in the face of ambiguous data? While there has been a substantial amount of research studying the biases that learners employ, there has been very little work on what sorts of biases are employed in the face of data that is ambiguous between phonological generalizations with different degrees of complexity. In this article, we present the results from three artificial language learning experiments that suggest that, at least for phonotactic sequence patterns, learners are able to keep track of multiple generalizations related to the same segmental co-occurrences; however, the generalizations they learn are only the simplest ones consistent with the data.
Advanced second language learners' perception of lexical tone contrasts
Mandarin tones are difficult for advanced L2 learners. But the difficulty comes primarily from the need to process tones lexically, and not from an inability to perceive tones phonetically.
Computational phonology today
Bill Idsardi and Jeff Heinz highlight important aspects of today's computational phonology.