基于web的阿尔茨海默氏症语音数据自动转录评估卡塔尔世界杯8强波胆分析成绩单比较与机器学习分析%A Soroski,Thomas %A da Cunha Vasco,Thiago %A牛顿-梅森，Sally %A Granby,Saffrin %A Lewis,Caitlin %A Harisinghani,Anuj %A Rizzo,Matteo %A Conati,Cristina %A Murray,Gabriel %A Carenini,Giuseppe %A Field,Thalia S %A Jang, hyyeju %+英属哥伦比亚大学理学院计算机科学系，BC省温哥华201-2366 Main Mall, V6T 1Z4, Canada, 1 604 822 3061，hyejuj@cs.ubc.ca %K阿尔茨海默病%K轻度认知障碍%K语音%K自然语言处理%K语音识别软件%K机器学习%K神经退行性疾病%K转录软件%K记忆%D 2022 %7 21.9.2022 %9原论文%J JMIR老化%G英语%X背景:用于医学研究的语音数据可以无创地大量收集。语音分析在诊断神经退行性疾病方面显示出前景。为了有效地利用语音数据，转录很重要，因为词汇内容中包含有价值的信息。人工转录虽然高度准确，但限制了潜在的可扩展性和与基于语言的筛选相关的成本节约。目的:为了更好地了解使用自动转录对神经退行性疾病的分类，即阿尔茨海默病(AD)、轻度认知障碍(MCI)或主观记忆投诉(SMC)与健康对照，我们比较了自动生成的转录本与手动校正的转录本。方法:我们招募了来自记忆诊所的个体(“患者”)，诊断为轻中度AD (n=44, 30%)， MCI (n=20, 13%)， SMC (n= 8,5%)，以及生活在社区的健康对照(n=77, 52%)。参与者被要求描述一幅标准化的图片，阅读一段文字，并回忆一段愉快的生活经历。我们通过检查转录置信度评分、转录错误率和机器学习分类准确性，将使用谷歌语音转文本软件生成的转录本与手动验证的转录本进行了比较。 For the classification tasks, logistic regression, Gaussian naive Bayes, and random forests were used. Results: The transcription software showed higher confidence scores (P<.001) and lower error rates (P>.05) for speech from healthy controls compared with patients. Classification models using human-verified transcripts significantly (P<.001) outperformed automatically generated transcript models for both spontaneous speech tasks. This comparison showed no difference in the reading task. Manually adding pauses to transcripts had no impact on classification performance. However, manually correcting both spontaneous speech tasks led to significantly higher performances in the machine learning models. Conclusions: We found that automatically transcribed speech data could be used to distinguish patients with a diagnosis of AD, MCI, or SMC from controls. We recommend a human verification step to improve the performance of automatic transcripts, especially for spontaneous tasks. Moreover, human verification can focus on correcting errors and adding punctuation to transcripts. However, manual addition of pauses is not needed, which can simplify the human verification step to more efficiently process large volumes of speech data. %M 36129754 %R 10.2196/33460 %U https://aging.www.mybigtv.com/2022/3/e33460 %U https://doi.org/10.2196/33460 %U http://www.ncbi.nlm.nih.gov/pubmed/36129754