Learning Biases in L2 Acquisition of Hong Kong Sign Language by Hearing Learners

GRF 2022/2023
(PI Youngah Do, Co-I Emmorey Karen, Sehyr Zed Sevcikova and Getzie Gabriel Paul)
General Research Fund (GRF), University Grants Council (UGC), Hong Kong
Amount: 971,972 HKD

When hearing individuals learn a second language, they rely on implicit knowledge from their first language in terms of sounds, words, grammar et cetera. But what happens when this knowledge does not apply? This is precisely the situation hearing individuals encounter when learning a sign language as a second language. Currently, there is no consensus on how people of primary linguistic experience with spoken language acquire a second language that is not spoken but signed. The main goal of our project is to understand how hearing individuals acquire Hong Kong Sign Language (HKSL) as a second language.

Specifically, we focus on how learning biases affect the learning of HKSL. One example of a learning bias is the structural bias, whereby learners prefer phonological structures involving simpler featural specifications over complex ones. Learning biases have been studied extensively in terms of how they affect the learning of spoken languages, but how they affect the learning of signed languages remains unexplored.

We propose a longitudinal study to uncover how learning biases affect hearing individual’s acquisition of HKSL as a second language. This longitudinal study is essentially videorecording, from multiple angles, 1-on-1 immersion lessons between Deaf HKSL instructors and hearing native Cantonese participants. The instructors and students will meet twice a week for 12 weeks and follow a curriculum set by the Professional Sign Language Training Centre (香港手語專業培訓中心). A longitudinal study in this closely documented format, for hearing learners with zero knowledge of signed languages, has never been done before.

Footage will be coded for phonological contrasts along with other factors, such as handshape complexity, and sign errors made by learners. Our database will allow for detailed erroranalysis to assess how L2 learning of HKSL is affected by learning biases. Also, our database will be open access so that interested researchers can explore how sign language pedagogy works in real-time and/or longitudinally.

Our project has two impact pathways: (1) contributing to the development of an automated translator of HKSL and (2) sign language pedagogy for hearers. For (1), we employ machine learning techniques to detect and analyze errors, which will serve as training data for a sign language translator model. For (2), we zone in on common errors, chart their progression over time, and propose corrective strategies for instructors to implement and raise learners’ awareness of errors.