@inproceedings{kahaer-etal-2026-synergizing,
    title = "Synergizing Semantic Anchors and Ordinal Smoothed Cross-Entropy for Speech Fluency Classification",
    author = "Kahaer, Mulati  and
      Ruzmamat, Sirajahmat  and
      Pang, XuDong  and
      Maimaitituerxun, Subinuer  and
      Kadeer, Zaokere  and
      Reheman, Abudurexiti  and
      Lu, Wenwen  and
      Zheng, Panpan  and
      Wumaier, Aishan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1551/",
    pages = "31018--31029",
    ISBN = "979-8-89176-395-1",
    abstract = "Speech fluency is a core indicator of second language proficiency and a critical component of Computer-Assisted Pronunciation Training (CAPT) systems. Accurate assessment requires models to perceive both macroscopic speech flow trends and microscopic local anomalies. However, existing methods struggle to bridge the semantic gap between static expert priors and dynamic temporal representations, while often overlooking the inherent ordinal nature of fluency scores. To address these challenges, we first construct a set of expert features targeting fluency disruptions and rhythmic regularity to provide explicit linguistic priors. Building on this, we propose the Multimodal Multi-Stream Fusion Classification (MMSFC) network. It employs a Mutual Cross-Attention (MCA) mechanism that leverages these expert features as ``semantic anchors'' to actively guide Whisper{'}s temporal representations and integrate decoder contexts, achieving deep interaction between global priors and local dynamics. Furthermore, we propose the Ordinal Smoothed Cross-Entropy (OSCE) loss. By constructing distance-aware soft target distributions coupled with confidence-adaptive smoothing and boundary enhancement, OSCE explicitly models ordinal relationships to resolve boundary ambiguity. Experiments on SpeechOcean762 show MMSFC achieves 83.40{\%} accuracy, significantly outperforming strong baselines. Notably, OSCE also demonstrates superior generalization potential in cross-domain CV and NLP tasks. Our code is available at \url{https://github.com/speech26ai/MMSFCCode}."
}