迈向客观且可解释的言语障碍评估：基于 CNN 与 Transformer 模型的比较分析
Towards objective and interpretable speech disorder assessment: a comparative analysis of CNN and transformer-based models
AI总结：
针对头颈癌患者病理性言语评估的主观性问题，提出基于自监督 Wav2Vec2 的音素分类模型，并通过多语料库实验证明其优于 CNN 基线且与感知评估相关，为可解释的临床言语分析提供支持。
摘要：
头颈癌（HNC）会显著影响患者的说话能力，进而影响其生活质量。评估病理性言语的常用指标具有主观性，因此需要自动化且无偏的评估方法。本研究提出一种基于自监督 Wav2Vec2 的模型，用于 HNC 患者的音素分类，以提高准确率并增强语音特征的区分度，从而服务于后续的可解释性分析。研究探讨了预训练数据集、模型规模以及微调数据集和参数的影响。在多个不同语料库上的评估揭示了 Wav2Vec2 架构的有效性，其表现优于先前工作中使用的基于 CNN 的方法。与感知测量结果的相关性也证实了该模型与受损言语分析的相关性。本工作通过利用复杂的自学习言语表征，为临床医生借助可解释方法更好地理解病理性言语铺平了道路。
英文摘要：
Head and Neck Cancers (HNC) significantly impact patients' ability to speak, affecting their quality of life. Commonly used metrics for assessing pathological speech are subjective, prompting the need for automated and unbiased evaluation methods. This study proposes a self-supervised Wav2Vec2-based model for phone classification with HNC patients, to enhance accuracy and improve the discrimination of phonetic features for subsequent interpretability purpose. The impact of pre-training datasets, model size, and fine-tuning datasets and parameters are explored. Evaluation on diverse corpora reveals the effectiveness of the Wav2Vec2 architecture, outperforming a CNN-based approach, used in previous work. Correlation with perceptual measures also affirms the model relevance for impaired speech analysis. This work paves the way for better understanding of pathological speech with interpretable approaches for clinicians, by leveraging complex self-learnt speech representations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
