论文标题
基于构象的老年语音识别系统的阿尔茨海默氏病检测
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection
论文作者
论文摘要
阿尔茨海默氏病（AD）的早期诊断对于促进预防性护理以延迟进一步发展至关重要。本文介绍了基于dementiabank Pitt copus建立的基于最新的构象体识别系统的开发，用于自动广告检测。通过纳入一组有目的设计的建模功能，包括基于域搜索的自动配置特异性构象异构体超参参数除外，还包括基于速度扰动和基于规格的数据增强训练的基线构象体系统可显着改善。使用学习隐藏单位贡献（LHUC）的细粒度老年人演讲者适应；以及与混合TDNN系统的基于两次通行的跨系统逆转。在48位老年人的评估数据上获得了总体单词错误率（相对34.8％）的总体单词错误率（相对34.8％）。使用最终系统的识别输出来提取文本特征，获得了最佳的基于语音识别的AD检测精度为91.7％。
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Conformer based speech recognition system built on the DementiaBank Pitt corpus for automatic AD detection. The baseline Conformer system trained with speed perturbation and SpecAugment based data augmentation is significantly improved by incorporating a set of purposefully designed modeling features, including neural architecture search based auto-configuration of domain-specific Conformer hyper-parameters in addition to parameter fine-tuning; fine-grained elderly speaker adaptation using learning hidden unit contributions (LHUC); and two-pass cross-system rescoring based combination with hybrid TDNN systems. An overall word error rate (WER) reduction of 13.6% absolute (34.8% relative) was obtained on the evaluation data of 48 elderly speakers. Using the final systems' recognition outputs to extract textual features, the best-published speech recognition based AD detection accuracy of 91.7% was obtained.
