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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.06972 |
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| _version_ | 1866915722398007296 |
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| author | Roll, Nathan Bhalerao, Pranav Bartelds, Martijn Pawar, Arjun Tatsumi, Yuka Ogunremi, Tolulope Shani, Chen Graham, Calbert Sumner, Meghan Jurafsky, Dan |
| author_facet | Roll, Nathan Bhalerao, Pranav Bartelds, Martijn Pawar, Arjun Tatsumi, Yuka Ogunremi, Tolulope Shani, Chen Graham, Calbert Sumner, Meghan Jurafsky, Dan |
| contents | In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their comparable performance stems from convergent processing strategies or distinct architectural inductive biases. We introduce Architectural Fingerprinting, a probing framework that isolates the effect of architecture on representation, and apply it to a controlled suite of 24 pre-trained encoders (39M-3.3B parameters). Our analysis reveals divergent hierarchies: Conformers implement a "Categorize Early" strategy, resolving phoneme categories 29% earlier in depth and speaker gender by 16% depth. In contrast, Transformers "Integrate Late," deferring phoneme, accent, and duration encoding to deep layers (49-57%). These fingerprints suggest design heuristics: Conformers' front-loaded categorization may benefit low-latency streaming, while Transformers' deep integration may favor tasks requiring rich context and cross-utterance normalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06972 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition Roll, Nathan Bhalerao, Pranav Bartelds, Martijn Pawar, Arjun Tatsumi, Yuka Ogunremi, Tolulope Shani, Chen Graham, Calbert Sumner, Meghan Jurafsky, Dan Computation and Language In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their comparable performance stems from convergent processing strategies or distinct architectural inductive biases. We introduce Architectural Fingerprinting, a probing framework that isolates the effect of architecture on representation, and apply it to a controlled suite of 24 pre-trained encoders (39M-3.3B parameters). Our analysis reveals divergent hierarchies: Conformers implement a "Categorize Early" strategy, resolving phoneme categories 29% earlier in depth and speaker gender by 16% depth. In contrast, Transformers "Integrate Late," deferring phoneme, accent, and duration encoding to deep layers (49-57%). These fingerprints suggest design heuristics: Conformers' front-loaded categorization may benefit low-latency streaming, while Transformers' deep integration may favor tasks requiring rich context and cross-utterance normalization. |
| title | Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.06972 |