Author: HKU Linguistics

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“An Expanded Model for Perceptual Norming: Insights From Japanese Ideophones” published in Topics in Cognitive Science

We report a new publication by Bonnie, Youngah, Arthur, and John in Topics in Cognitive Science. The paper titled “An expanded model for perceptual norming: Insights from Japanese ideophones.” It investigates how sensory experience is encoded in Japanese ideophones, a class of vivid words often described as sound‑symbolic.

Using perceptual strength ratings across 13 sensory dimensions, the study moves beyond the traditional five‑ or six‑sense model. The results show that so‑called visual dominance is driven mainly by movement, while other visual properties such as shape and light or colour behave differently and connect to other senses in distinct ways. For example, movement patterns with sound and internal bodily sensations, shape with touch, and colour with taste and smell. The study also finds meaningful structure within interoception, with pain separating from emotions and bodily feelings.

Overall, the findings demonstrate that finer‑grained sensory models reveal cross‑modal relationships that are hidden in coarser approaches, highlighting the value of expanded perceptual norming for the study of iconicity and meaning.

McLaren, B., Do, Y., Thompson, A.L., & Husman, J. (2026). An expanded model for perceptual norming: Insights from Japanese ideophones. Topics in Cognitive Science. open_in_new DOI

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Modeling Tone Sandhi Learning from Surface Evidence: HISPhonCog 2026 Presentation

Last week, Frank, Ming, and Youngah presented their talk titled “Learning tone sandhi from surface forms: A modeling approach” at HISPhonCog 2026 in Seoul, Korea. 

Their presentation examined how learners acquire tone sandhi patterns using only surface tonal forms, without direct access to underlying representations. They used neural network models trained on artificial languages to explore challenges posed by various types of alternations—especially mergers and context-conditioned rules—and how factors like positional restrictions and diagnostic contexts influence generalization.

Key findings showed that while surface mergers can make category induction more difficult, sufficient non-neutralizing evidence enables models to maintain abstract distinctions. The results offer computational insights into the conditions that support or hinder the learning of tone sandhi patterns from naturalistic, incomplete input.

Tan, F. L., Liu, M., & Do, Y. (2026, May 22–23). Learning tone sandhi from surface forms: A modeling approach [Paper presentation]. Hanyang International Symposium on Phonetics and Cognitive Sciences of Language (HISPhonCog), Seoul, Korea.

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“Modeling the impact of prenatal audio attenuation on speech sound learning” published in the Journal of Experimental Psychology: Learning, Memory, and Cognition 

We are pleased to share a new publication from Ivy, Frank and Youngah in the Journal of Experimental Psychology: Learning, Memory, and Cognition. The paper, titled “Modeling the impact of prenatal audio attenuation on speech sound learning,” examines how human infants appear to have substantial knowledge of the sound structure of their native language at birth, despite the fact that the uterine environment strongly limits auditory input to low-frequency sounds.

The study explores whether this prenatal low-frequency exposure may actually support later speech sound learning rather than hinder it. To address this question, the authors trained neural network models in two stages designed to simulate prenatal and postnatal learning. During the prenatal stage, models were exposed to speech that was either naturally low-pass filtered, artificially high-pass filtered, or unfiltered. After birth, all models were trained on full-frequency speech. Three different neural network architectures were examined, including a long short-term memory network, a convolutional neural network, and a residual neural network, to test whether the effects generalised across learning systems.

Across architectures, the results showed that prenatal exposure to low-frequency speech led to faster and more effective phonetic learning once full-frequency input became available. In contrast, exposure to high-frequency–only speech was less beneficial during prenatal learning. These findings suggest that the low-frequency sounds available before birth may provide a useful foundation that helps infants extrapolate to the richer speech input they encounter after birth, offering a computational explanation for early speech sound knowledge.

Zheng, S., Tan, F. & Do, Y. (2026). Modeling the impact of prenatal audio attenuation on speech sound learning. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. open_in_newDOI

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“Learning and processing sound correspondences during dialect contact” Xiaoyu shares research at Peking University

Xiaoyu was invited to present a talk at the Department of Chinese Language and Literature, Peking University (PKU). In his presentation, he shared behavioral and neurophysiological evidence regarding how speakers learn and process sound correspondence during dialect contact, concluding with discussion on the mental lexicon of bidialectals.

PKU’s Chinese linguists have a long-standing tradition of studying the sound correspondences among Chinese varieties and between Chinese languages and neighboring languages. Their work has made significant contributions to fields such as historical-comparative linguistics, language variation and change, and dialectology. Xiaoyu’s talk approaches sound correspondences from alternative perspectives, sharing new findings derived from artificial language learning and neurolinguistic methods.

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Sign Language Demo at the Linguistics Information Booth

Today, our lab was delighted to take part in the General Linguistics information booth, held from 10:00 am to 5:00 pm at the Faculty Lounge (CPD 4.30), Run Run Shaw Tower. During the event, we showcased our Sign Language Demo featuring the Handshape Detection Machine Learning Model from our Hong Kong Sign Language (HKSL) project.

The demo introduced visitors to how we document HKSL and its rich visual characteristics, with a focus on developing a machine learning algorithm that can detect handshapes—one of the most important building blocks of sign language. By training our model on collected HKSL citation signs, the system is able to recognise handshapes rapidly and accurately.

The booth attracted many visitors with an interest in linguistics and sign language. We enjoyed engaging conversations and shared our research through live demonstrations. The positive response and curiosity from visitors made the event a rewarding and encouraging experience for our team.

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Sharing Our Work at SCiL 2026: Two Accepted Papers

Exciting news! Two papers from our lab have been accepted to this year’s SCiL!

Here are the titles and authors:

  1. “Roles of Predictability and Acoustic Distance in Sound Discrimination via Contrastive Learning” from Shuhao and Youngah.
  2. “The Development of Spectral and Temporal Encodings in Speech Sounds” from Frank and Youngah.

We’re thrilled to share our work with the computational linguistics community this summer—stay tuned for more updates!

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Zhihao Defends His Thesis on Asian Tone Systems

Zhihao just defended his thesis—huge congratulations! 🎉 His dissertation explores the internal structure of contour tone systems in Asian tonal languages, which remains under-researched compared to consonants and vowels. It examines three key aspects: the contour shapes of tones, their markedness hierarchy, and perceptual similarity based on auditory and cognitive factors. The study employs experimental production data, corpus analysis of loanword tonal patterns, and statistical methods such as rhyming frequency to analyze these structures. Through case studies of Chinese dialects, it aims to deepen understanding of how contour tones are organized and perceived, with future research expected to expand these insights across more Asian languages. Not only did he conquer his dissertation, but he also scored a tenure-track position—stay tuned for the exciting details! We’re all so proud and thrilled for you, Zhihao!

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“Iconicity and semantic transparency in Hong Kong Sign Language: Evidence from ratings and three guessing paradigms” published in Language and Cognition

We are pleased to announce the publication of a new article in Language and Cognition by Arthur, Aaron, Mavies, Rachel, Judy and Youngah, titled Iconicity and semantic transparency in Hong Kong Sign Language: Evidence from ratings and three guessing paradigms.

This study investigates how strongly signs in Hong Kong Sign Language (HKSL) are perceived to resemble their meanings, a property known as iconicity, and how this relates to how easily meanings can be inferred by people with no knowledge of HKSL. The authors collected iconicity ratings for 972 HKSL signs from both Deaf native HKSL signers and hearing Cantonese-speaking non-signers, and examined how these ratings relate to performance in several meaning‑guessing tasks.

Results show that HKSL signs are rated as comparably iconic to signs in other well‑studied sign languages, including American Sign Language and Israeli Sign Language, with Deaf signers assigning higher iconicity ratings overall. Across tasks, signs rated as more iconic were also more likely to be guessed correctly by hearing non-signers. Importantly, the study shows that semantic transparency is not all‑or‑nothing: when contextual information is provided through multiple‑choice options, many signs become “translucent,” allowing accurate inference, whereas open‑ended guessing without context is much more difficult.

By combining large‑scale iconicity ratings with multiple guessing paradigms and cross‑linguistic comparisons, this work provides a new empirical baseline for studying iconicity and semantic transparency in HKSL and contributes to broader discussions about how form–meaning relationships are perceived across sign languages.

Thompson, A., Chik, A., Ngai, M., Chen, R., Ng, J., & Do, Y. (2026). Iconicity and semantic transparency in Hong Kong Sign Language: Evidence from ratings and three guessing paradigms. Language and Cognition, 18, Article e21. open_in_newDOI

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“Modeling Prosodic Development with Prenatal Audio Attenuation” published in the Proceedings of the Annual Meetings on Phonology.

We are pleased to share a new publication from Frank, Shuang, Ming, and Youngah in the Proceedings of the Annual Meetings on Phonology. The paper, titled “Modeling Prosodic Development with Prenatal Audio Attenuation,” investigates how the sound environment before birth may shape early prosodic learning—the ability to perceive patterns such as stress and tone in speech.

Preterm infants often experience delayed language development, and one contributing factor may be the reduced duration of prenatal auditory exposure. To better understand this, the authors used convolutional neural networks to simulate infants’ early learning environment. The models were first trained on low‑frequency audio, reflecting the kinds of sounds fetuses can hear in utero, before being exposed to full‑frequency speech that resembles postnatal auditory input.

The study shows that longer exposure to low‑frequency audio provides an initial advantage for learning stress and tone patterns, though this early benefit fades over time. Interestingly, the simulations also reveal that learning improves even more when models are trained on full‑frequency audio for the same duration, suggesting that infants may rely on a wider range of acoustic cues than previously assumed. These findings underscore the importance of both the quantity and quality of auditory input in early prosodic development.

Thompson, A., Chik, A., Ngai, M., Chen, R., Ng, J., & Do, Y (2026). Iconicity and semantic transparency in Hong Kong Sign Language: Evidence from ratings and three guessing paradigms. Language and Cognition, 2(1). open_in_newDOI

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“Investigating the Tone–Segment Asymmetry in Phonological Counting: A Learnability Experiment” published in the Proceedings of the Annual Meetings on Phonology.

We are pleased to announce a new publication by Jian, Hanna, Youngah and Jesse in the Proceedings of the Annual Meetings on Phonology. The paper, titled “Investigating the Tone-Segment Asymmetry in Phonological Counting: A Learnability Experiment,” examines how learners acquire rules that rely on counting either tones or segments, two fundamental components of spoken language.

Tone-segment asymmetry has long attracted attention in phonological theory, with many proposals suggesting that tones and segments behave differently in how they pattern across languages. This study provides the first experimental test of whether these typological differences are connected to how easily such patterns can be learned. Using an artificial-language learning paradigm, the authors compared learners’ ability to acquire a tonal counting rule with their ability to learn a structurally parallel segmental rule.

The results reveal that an unattested segmental counting pattern is significantly more difficult for learners than its tonal equivalent. This asymmetry in learnability suggests that cognitive biases may contribute to the distribution of tone‑ and segment‑based counting patterns observed cross‑linguistically.

Cui, J., Shine, H., Do, Y., & Snedeker, J. (2026). Investigating the tone-segment asymmetry in phonological counting: A learnability experiment. Proceedings of the Annual Meetings on Phonology, 2(1). open_in_newDOI