McGill University

About: McGill University is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 72688 authors who have published 162565 publications receiving 6966523 citations. The organization is also known as: Royal institution of advanced learning & University of McGill College.

Early brain development in infants at high risk for autism spectrum disorder

Abstract: Autism spectrum disorder (ASD) is associated with brain overgrowth, but it has been unclear how this relates to behavioural symptoms. In a longitudinal neuroimaging study of young children at high familial risk of autism, Heather Hazlett and colleagues now show that high-risk children who receive a diagnosis of ASD at 24 months of age had an increased cortical growth rate at 612 months. Early overgrowth in high-risk children is associated with social impairments at 24 months, and imaging data obtained at 6 and 12 months can predict an ASD diagnosis at 24 months in high-risk children. These findings indicate that differences in the developmental trajectory towards ASD emerge as early as the first year of life.

Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi.

Abstract: We present the Montreal Forced Aligner (MFA), a new opensource system for speech-text alignment. MFA is an update to the Prosodylab-Aligner, and maintains its key functionality of trainability on new data, as well as incorporating improved architecture (triphone acoustic models and speaker adaptation), and other features. MFA uses Kaldi instead of HTK, allowing MFA to be distributed as a stand-alone package, and to exploit parallel processing for computationally-intensive training and scaling to larger datasets. We evaluate MFA’s performance on aligning word and phone boundaries in English conversational and laboratory speech, relative to human-annotated boundaries, focusing on the effects of aligner architecture and training on the data to be aligned. MFA performs well relative to two existing open-source aligners with simpler architecture (Prosodylab-Aligner and FAVE), and both its improved architecture and training on data to be aligned generally result in more accurate boundaries.