Abstract: End-to-end modeling and deep learning have significantly advanced human language technology; recent examples include large language models. However, to ensure inclusivity and extend such benefits to more people, there remains substantial work ahead. In this talk, we investigate how to make EdTech more inclusive for language learning applications in four dimensions: (1) Language Diversity, (2) Student Age Groups, (3) Human-Computer Interaction Styles, and (4) Intrinsic Subjectivity in Evaluations. We illustrate how speech science and statistical machine learning can elegantly blend with neural modelling approaches to address technical challenges such as data sparsity, feature bias, and explainability. We will share our experience in developing AI technology to help students learn English, Mandarin Chinese, Malay, and Tamil. These languages span across various linguistic families and possess varying degrees of linguistic resources suitable for computational approaches. Our endeavours have led to government deployment and commercial spin-offs, serving as valuable case studies for AI's role in cultural and linguistic heritage preservation.

Bio: Nancy F. Chen received her PhD from MIT and Harvard. Her group at A*STAR works on generative AI in speech, language, and conversational technology. Her research has been applied to education, defense, healthcare, and media/journalism. Dr. Chen has published 100+ papers and supervised 100+ students/staff. She has won awards from IEEE, Microsoft, NIH, P&G, UNESCO, L’Oréal, SIGDIAL, APSIPA, MICCAI. She is IEEE SPS Distinguished Lecturer (2023-2024), Program Chair of ICLR 2023, A*STAR Fellow (2023), Board Member of ISCA (2021-2025), and Singapore 100 Women in Tech (2021). Technology from her team has led to commercial spin-offs and government deployment. Prior to A*STAR, she worked at MIT Lincoln Lab. For more info: http://alum.mit.edu/www/nancychen.