Jarrold William, Rofes Adria, Wilson Stephen, Pressman Peter, Stabler Edward, Gorno-Tempini Marilu
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5831-5837. doi: 10.1109/EMBC44109.2020.9176185.
Clinicians often use speech to characterize neurodegenerative disorders. Such characterizations require clinical judgment, which is subjective and can require extensive training. Quantitative Production Analysis (QPA) can be used to obtain objective quantifiable assessments of patient functioning. However, such human-based analyses of speech are costly and time consuming. Inexpensive off-the-shelf technologies such as speech recognition and part of speech taggers may avoid these problems. This study evaluates the ability of an automatic speech to text transcription system and a part of speech tagger to assist with measuring pronoun and verb ratios, measures based on QPA. Five participant groups provided spontaneous speech samples. One group consisted of healthy controls, while the remaining groups represented four subtypes of frontotemporal dementia. Findings indicated measurement of pronoun and verb ratio was robust despite errors introduced by automatic transcription and the tagger and despite these off-the-shelf products not having been trained on the language obtained from speech of the included population.
临床医生经常通过言语来描述神经退行性疾病。这样的描述需要临床判断，而临床判断是主观的，并且可能需要大量的培训。定量产出分析（QPA）可用于获得对患者功能的客观可量化评估。然而，这种基于人工的言语分析成本高且耗时。诸如语音识别和词性标注器等廉价的现成技术可能会避免这些问题。本研究评估了一个自动语音转文本转录系统和一个词性标注器在协助测量代词和动词比率方面的能力，这些测量基于QPA。五个参与者小组提供了自发言语样本。一组由健康对照组成，其余小组代表额颞叶痴呆的四种亚型。研究结果表明，尽管自动转录和标注器会引入错误，且这些现成产品并未针对从纳入人群的言语中获得的语言进行训练，但代词和动词比率的测量依然可靠。
