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I am interested in investigating how typical and atypical populations perceive and produce native and nonnative speech sounds and various language structures. My broader research interests include experimental phonetics, speech and language acquisition, the interplay between speech acquisition and linguistic/biological/cognitive and other factors, bilingualism, and machine learning. This extends across a spectrum of populations, including both typical and atypical individuals (i.e. those with communication disorders), and spans various age groups, from children to adults. To achieve these goals, I conduct acoustic analysis of speech sounds, employ behavioral, neuropsychological, and psychometric tests, and leverage AI algorithms to predict speech and language patterns. I employ a diverse range of statistical methods for data analysis, encompassing traditional frequentist approaches like mixed-effects models, as well as advanced methodologies such as Bayesian regression models. My research focuses on the following key areas of investigation:

  • Perception and production of nonnative speech sounds (consonants and vowels) by mono-/bi-/multivarietal speakers

  • Differences between bilingual/bidialectal and monolingual/monodialectal speakers in nonnative speech perception and production

  • Perception and production of heritage language sounds

  • Relationship between speech perception and production 

  • Models of nonnative speech acquisition (I have developed the Universal Perceptual Model; UPM)

  • Phonological training targetting the abilities to perceive and produce speech sounds

  • Acoustic features of native sounds in various conditions (e.g., masked conditions, whispered speech, different recording devices)

  • Role of linguistic (e.g., profieciency level, orthography, vocabulary size), biological (e.g., age), cognitive (e.g., phonological short-term memory, nonverbal intelligence, attention control), and other factors in speech acquisition

  • Perception and production of language structures (phonology, grammar, and semantics) by children with developmental language disorder

  • Acoustic features of sounds produced by individuals with communication-affecting conditions such as aphasia, apraxia of speech, Alzheimer's disease, Autism Spectrum Disorders, etc.

  • Artificial Intelligence algorithms for the prediction of crosslinguistic sound classification and discrimination of sound contrasts

  • Artificial Intelligence algorithms for the prediction of atypical speech

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