Fengyue Zhao
Fengyue Zhao 赵丰悦

PhD Candidate in linguistics

fz227@cornell.edu

About Me

Hi! I'm Fengyue Lisa Zhao, a fifth-year PhD candidate in the Department of Linguistics at Cornell University. My research interests lie in phonetics, phonology, computational linguistics, and psycholinguistics. I am part of the Cornell Phonetics Lab and the Computational Psycholinguistics Discussions research group.

Updated Aug 2026

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Selected Projects
Laryngeal Elevation in Native and Non-native Lexical Tone Production
Unpredictable Temporal Feedback Perturbation Dissociates Mismatch Detection from Correction
Distributional Learning Across Contexts: Learning Cantonese Tones in Naturalistic Speech
Distributional Learning

Distributional Learning Across Contexts: Learning Cantonese Tones in Naturalistic Speech

- Fengyue Lisa Zhao, Jennifer Kuo. The Role of Contextual Variation in Learning Cantonese Tones from Naturalistic Speech. Proc. Interspeech 2025, 4453-4457, doi:10.21437/Interspeech.2025-1876 Motivation Infants initially discriminate most sound contrasts but quickly attune to those of their native language. This raises the question: how do infants identify the relevant acoustic dimensions for learning phonetic categories? The distributional learning account proposes that infants track the distribution of sounds, and identify acoustic dimensions as contrastive if their distribution has two or more distinct peaks (i.e. multimodal distributions) [1]. However, while multimodality appear in controlled experiments, they are rarely found in naturalistic, highly variable speech, suggesting that multimodality is not a reliable way to identify contrastive dimensions [2]. Recent work comparing languages with/without vowel length contrasts suggests that even without multimodality, contrastive dimensions show more contextual variability: when a dimension is contrastive, the shape of its distribution will vary more across contexts [3]. The distributional learning across contexts hypothesis proposes that infants utilize this contextual variability to distinguish phonetic categories. This study tests this hypothesis by examining Hong Kong Cantonese tones, exploring whether ease of acquiring different tonal contrasts is linked to their contextual variability in distribution shape. Cantonese serves as a valuable test case due to the overlapping acoustic distributions between its six tones: high-level (T1), high-rising (T2), mid-level (T3), low-falling (T4), low-rising (T5), and low-level (T6).

Syllable Position Prominence in Unsupervised Neural Network Segment Categorization
Unsupervised Learning

Syllable Position Prominence in Unsupervised Neural Network Segment Categorization

- Fengyue Zhao, Sam Tilsen. Syllable Position Prominence in Unsupervised Neural Network Segment Categorization. LabPhon 19. June 27 - 29 2024. Hanyang University, Seoul, South Korea. Motivation English obstruents exhibit diverse phonetic realizations across syllable positions, like /t/ and /p/ in words such as top and pot [1]. Linguistically we assume that phone identity—(e.g. /p/ vs. /t/) is a strong predictor of representational similarity, while syllable position—e.g. onset vs. coda—is perhaps a secondary factor. But is this always the case? Unsupervised learning in neural networks presents a practical approach for exploring this interplay, because it does not require presuppositions about phonological categories such as segments and syllable. Previous studies [2, 3] have demonstrated the capacity of neural networks to learn abstract representations from acoustic signals. This study employed an unsupervised autoencoder neural network to explore the correlation between phonological categories and network-learned representations. Surprisingly, we found that for consonants, syllable position plays a larger role in representational similarity than phone identity.