Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Department of Rehabilitation Medicine, Incheon Workers' Compensation Hospital, Incheon, Korea.
J Korean Med Sci. 2019 Apr 8;34(13):e108. doi: 10.3346/jkms.2019.34.e108.
The gold standard in dysarthria assessment involves subjective analysis by a speech-language pathologist (SLP). We aimed to investigate the feasibility of dysarthria assessment using automatic speech recognition.
We developed an automatic speech recognition based software to assess dysarthria severity using hidden Markov models (HMMs). Word-specific HMMs were trained using the utterances from one hundred healthy individuals. Twenty-eight patients with dysarthria caused by neurological disorders, including stroke, traumatic brain injury, and Parkinson's disease were participated and their utterances were recorded. The utterances of 37 words from the Assessment of Phonology and Articulation for Children test were recorded in a quiet control booth in both groups. Patients were asked to repeat the recordings for evaluating the test-retest reliability. Patients' utterances were evaluated by two experienced SLPs, and the consonant production accuracy was calculated as a measure of dysarthria severity. The trained HMMs were also employed to evaluate the patients' utterances by calculating the averaged log likelihood (aLL) as the fitness of the spoken word to the word-specific HMM.
The consonant production accuracy reported by the SLPs strongly correlated ( = 0.808) with the aLL, and the aLL showed excellent test-retest reliability (intraclass correlation coefficient, 0.964).
This leads to the conclusion that dysarthria assessment using a one-word speech recognition system based on word-specific HMMs is feasible in neurological disorders.
构音障碍评估的金标准涉及言语语言病理学家（SLP）的主观分析。我们旨在研究使用自动语音识别进行构音障碍评估的可行性。
我们开发了一种基于自动语音识别的软件，使用隐马尔可夫模型（HMM）评估构音障碍的严重程度。使用一百名健康个体的话语来训练特定于单词的 HMM。 28 名患有由神经障碍引起的构音障碍的患者，包括中风、创伤性脑损伤和帕金森病，参与并记录了他们的话语。在安静的控制舱中，两组都记录了来自儿童语音和发音评估的 37 个单词的发音。要求患者重复录音以评估测试-重测信度。两名经验丰富的 SLP 评估患者的发音，并用辅音产生准确性作为构音障碍严重程度的衡量标准。还使用经过训练的 HMM 通过计算平均对数似然（aLL）来评估患者的发音，作为说话单词与特定于单词的 HMM 的拟合度。
SLP 报告的辅音产生准确性与 aLL 强烈相关（r=0.808），aLL 显示出极好的测试-重测信度（组内相关系数，0.964）。
这得出的结论是，使用基于特定于单词的 HMM 的单字语音识别系统评估构音障碍在神经障碍中是可行的。
