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.
Mar 30, 2024
