与声音分析相关的关节发音障碍:1例报告

K. GovathiNikhila
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引用次数: 1

摘要

中风是全球第二大死亡原因,它造成的脑损伤会在几个方面影响沟通。构音障碍的语音分析具有挑战性,因为该障碍的复杂性及其对语音产生系统的影响。在本研究中,我们报告一名56岁男性患者就诊于Medanta医院,他有高血压病史,主诉为右上肢无力和言语不清,后来临床和无线电诊断为左中动脉MCA梗死。随后,在第3天对患者进行了语音和语言评估,并根据法国构音障碍评估量表诊断为痉挛性构音障碍,随后使用PRAAT软件进行了详细的语音分析并分析了语音特征。语音分析基本上是将语音信号分解为语音参数,并对得到的特征进行处理。本文提取的特征有:频率、音高、声音强度、共振峰、语音速率以及Jitter (local)、Jitter (local, absolute)、Jitter (rap)、Jitter (ppq5)、Jitter (ddp)、Shimmer (local)、Shimmer (local, dB)、Shimmer (apq3)、Shimmer (apq5)、Shimmer (apq11)、Shimmer (dda)、Harmonic系数等脉冲函数。总之,我们总结了痉挛性构音障碍的语音参数,这揭示了语音质量的有趣数据,有助于临床医生更好地管理。然而,需要进行大样本研究。
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Dysarthrophonia in Association with Voice Analysis: A Case Report
Stroke is the second leading cause of death worldwide and the brain damage caused by it can affect communication in several aspects. Voice analysis in dysarthria is challenging because of the complexity of the disorder and its effects on the speech production system. In this study we are presenting a 56-years-old male who was visited to Medanta Hospital with history of hypertension and chief complaint of Right upper limb weakness and slurred speech to the Emergency and later Clinically and Radio logically Diagnosed as LT MCA Infarct. Later, on the day 3 the patient has undergone Speech and Language Evaluation and Diagnosed with Spastic Dysarthria based on Frenched Dysarthria Assessment scale and later a detail Voice Analysis was done with using PRAAT software and analysed voice features. Voice analysis basically deals with decomposition of voice signal into voice parameters for processing the resulted features in desirable application. The features that are extracted in this paper are: frequency, pitch, voice intensity, formant, speech rate and pulse functions like Jitter (local), Jitter (local, absolute), Jitter (rap), Jitter (ppq5), Jitter (ddp), Shimmer (local), Shimmer (local, dB), Shimmer (apq3), Shimmer (apq5), Shimmer (apq11), Shimmer (dda) and Harmonic coefficients. Over all, we conclude with the voice parameters in spastic dysarthria which reveals interesting data on the voice quality with features which helps the clinician for better management. However, large sample study is required.
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