Fengyue Zhao
Fengyue Zhao 赵丰悦

PhD candidate in linguistics

About Me

Hi. My name is Fengyue (Lisa) Zhao. I am a fourth-year PhD candidate at the Department of Linguistics, 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 February 2025

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Selected Projects
Distributional Learning Across Contexts: Learning Cantonese Tones in Naturalistic Speech

Distributional Learning Across Contexts: Learning Cantonese Tones in Naturalistic Speech

- 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 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).