LOPA: Enhancing Spoken Language Assessment via Latent Ordinal Prototype Alignment
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Computer Science > Computation and Language
Title:LOPA: Enhancing Spoken Language Assessment via Latent Ordinal Prototype Alignment
Abstract:Fueled by increasing model scale and multimodal inputs, Multimodal Large Language Models (MLLMs) have emerged as a promising paradigm for Spoken Language Assessment (SLA). While effective, this paradigm often overlooks the intrinsic ordinal structure of language acquisition. This paper works around the necessity of large-scale MLLMs by introducing Latent Ordinal Prototype Alignment (LOPA) for SLA, a prototype-based regularizer that enforces an ordinal geometric prior directly on the latent space. Coupled with Semantic-Anchored Layer Routing (SALR), which adaptively harvests multi-depth representations from a frozen Whisper encoder, our framework achieves an RMSE of 0.361. This performance rivals billion-parameter systems without the need for LLM-based fine-tuning. Further analysis reveals that SALR's synergy with LOPA offers interpretable, criterion-aligned preferences, thereby supporting an efficient and ordinal-aware modeling alternative to current scaling-centric models for SLA.
| Subjects: | Computation and Language (cs.CL); Multimedia (cs.MM) |
| Cite as: | arXiv:2606.31310 [cs.CL] |
| (or arXiv:2606.31310v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31310
arXiv-issued DOI via DataCite (pending registration)
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