用生物标志物监督提升亨廷顿病语音识别准确率
Huntington Disease Automatic Speech Recognition with Biomarker Supervision
- 引入生物标志物辅助训练，引导模型关注疾病特异性语音特征
- 识别错误模式随病情严重程度变化，非均匀改善，降低误识率至4.95%
- 首个在高保真临床语料上系统评估多种语音识别模型的研究
针对亨廷顿病（HD）的病理语音自动语音识别（ASR）研究仍不充分，其不规则时序、不稳定发声和构音畸变给现有模型带来挑战。本文基于此前未用于端到端训练的高保真临床语音语料，系统比较多种ASR架构的性能，分析词错误率（WER）及替换、删除、插入错误模式。结果表明，不同模型在HD语音下呈现特定错误模式，其中Parakeet-TDT优于编码器-解码器与CTC基线模型。通过针对性适配，将WER从6.99%降至4.95%；同时提出基于生物标志物的辅助监督方法，发现错误行为随病情严重程度呈差异化重塑，而非统一改善。所有代码与模型已开源。
原文摘要 · Abstract (English)
Automatic speech recognition (ASR) for pathological speech remains underexplored, especially for Huntington's disease (HD), where irregular timing, unstable phonation, and articulatory distortion challenge current models. We present a systematic HD-ASR study using a high-fidelity clinical speech corpus not previously used for end-to-end ASR training. We compare multiple ASR families under a unified evaluation, analyzing WER as well as substitution, deletion, and insertion patterns. HD speech induces architecture-specific error regimes, with Parakeet-TDT outperforming encoder-decoder and CTC baselines. HD-specific adaptation reduces WER from 6.99% to 4.95% and we also propose a method for using biomarker-based auxiliary supervision and analyze how error behavior is reshaped in severity-dependent ways rather than uniformly improving WER. We open-source all code and models.
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