aiqbench analysis
This study investigates whether audio language models align the internal representations of heard and read phonemes, specifically testing if distinctive features like voicing share a consistent direction in the decoder across both modalities. The findings reveal that alignment is highly inconsistent across different model families, with only specific instances showing agreement beyond random chance, suggesting that model architecture dictates this capability more than parameter count.
Do Audio Language Models Hear and Read Distinctive Features Alike?
arXiv:2609.30167v1 Announce Type: cross Abstract: Audio language models pass speech and text through a single decoder. We ask whether that decoder represents a distinctive feature in the same direction when a phoneme is heard and when it is read. For minimal pairs of phonemes differing in one feature, we take the offset between the two members' mean representations. Averaging those offsets gives a direction for each stream, and we measure the cosine between the two. Because the two streams already agree about arbitrary phoneme pairs, we compare every measure against a reference built from random pairings rather than against zero. We apply this to 6 models, 7 features and 15 languages from 11 families. Only voicing in the two Qwen2.5-Omni models exceeds that reference after correction for multiple testing, and the reference varies by a factor of seven between models. In three of the six models, voicing has one direction in audio across the 14 languages with enough minimal pairs to measure it, and every language pair agrees in two of them. The model family, not the model size, predicts which stream represents a feature.
Title:Do Audio Language Models Hear and Read Distinctive Features Alike?
Abstract:Audio language models pass speech and text through a single decoder. We ask whether that decoder represents a distinctive feature in the same direction when a phoneme is heard and when it is read. For minimal pairs of phonemes differing in one feature, we take the offset between the two members' mean representations. Averaging those offsets gives a direction for each stream, and we measure the cosine between the two. Because the two streams already agree about arbitrary phoneme pairs, we compare every measure against a reference built from random pairings rather than against zero. We apply this to 6 models, 7 features and 15 languages from 11 families. Only voicing in the two Qwen2.5-Omni models exceeds that reference after correction for multiple testing, and the reference varies by a factor of seven between models. In three of the six models, voicing has one direction in audio across the 14 languages with enough minimal pairs to measure it, and every language pair agrees in two of them. The model family, not the model size, predicts which stream represents a feature.
Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Applications (stat.AP)
arXiv:2609.30167 [cs.CL]
(or arXiv:2609.30167v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.30167 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 292 words
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