This paper presents a fully automated approach for identifying speech
anomalies from voice recordings to aid in the assessment of speech impairments.
By combining Connectionist Temporal Classification (CTC) and
encoder-decoder-based automatic speech recognition models, we generate rich
acoustic and clean transcripts. We then apply several natural language
processing methods to extract features from these transcripts to produce
prototypes of healthy speech. Basic distance measures from these prototypes
serve as input features for standard machine learning classifiers, yielding
human-level accuracy for the distinction between recordings of people with
aphasia and a healthy control group. Furthermore, the most frequently occurring
aphasia types can be distinguished with 90% accuracy. The pipeline is directly
applicable to other diseases and languages, showing promise for robustly
extracting diagnostic speech biomarkers.

该研究提出了一种全自动的方法来识别语音记录中的语音异常，以帮助评估语音障碍。结合连续时间分类（CTC）和基于编码器 - 解码器的自动语音识别模型，生成丰富的声学和干净的转录本。然后，应用几种自然语言处理方法从这些转录本中提取特征，以产生健康语音的原型。利用这些原型的基本距离度量作为标准机器学习分类器的输入特征，可以实现与人类水平相当的准确性来区分患有失语症的人与健康对照组的记录。此外，可以以 90% 的准确性区分最常见的失语症类型。该流程可直接应用于其他疾病和语言，并显示出从语音诊断标志提取的强大前景。