A Technique for Isolating Lexically-Independent Phonetic Dependencies in Generative CNNs

• URL: http://arxiv.org/abs/2506.09218v1
• Date: Tue, 10 Jun 2025 20:22:33 GMT
• Title: A Technique for Isolating Lexically-Independent Phonetic Dependencies in Generative CNNs
• Authors: Bruno Ferenc Šegedin,
• Abstract summary: The ability of deep neural networks (DNNs) to represent phonotactic generalizations derived from lexical learning remains an open question.<n>This study investigates the lexically-invariant generalization capacity of generative convolutional neural networks (CNNs) trained on raw audio waveforms of lexical items.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability of deep neural networks (DNNs) to represent phonotactic generalizations derived from lexical learning remains an open question. This study (1) investigates the lexically-invariant generalization capacity of generative convolutional neural networks (CNNs) trained on raw audio waveforms of lexical items and (2) explores the consequences of shrinking the fully-connected layer (FC) bottleneck from 1024 channels to 8 before training. Ultimately, a novel technique for probing a model's lexically-independent generalizations is proposed that works only under the narrow FC bottleneck: generating audio outputs by bypassing the FC and inputting randomized feature maps into the convolutional block. These outputs are equally biased by a phonotactic restriction in training as are outputs generated with the FC. This result shows that the convolutional layers can dynamically generalize phonetic dependencies beyond lexically-constrained configurations learned by the FC.

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