根据特征复杂性的含义模型,通过模糊建模对语音偏差的严重程度进行分类

Ana Rita Brancalioni
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引用次数: 1

摘要

人类沟通障碍硕士学位论文研究生课程圣玛丽亚联邦大学,RS,巴西。基于特征复杂性蕴涵模型的语音障碍严重程度模糊分类建议作者:ANA RITA BRANCALIONI顾问:MÁRCIA KESKE-SOARES合作顾问:KARINE FAVERZANI MAGNAGO本研究旨在基于特征复杂性蕴涵模型- IMFC (MOTA, 1996),考虑Rangel(1998)的不足,提出语音障碍严重程度的定量分类方法。基于模糊模型对建议进行量化;为此,使用Mamdani的最小推理方法和区域中心去模糊化方法,在模糊规则系统的基础上,创建了一个并行处理的语言模糊模型。该模型包含三个输入变量:路径过程,复杂程度和音素的获取,用添加了模糊子集(每个变量有三个子集)的语言术语来描述。边界的确定遵循了基于IMFC和研究人员经验的标准和推断。模型输出变量是基于四个模糊子集的严重程度语音障碍指数:严重、中度严重、中度轻度和轻度。使用模糊语言模型计算样本中所有204个偏离语音系统的严重语音障碍指数,并在MATLAB模糊工具箱中运行(2009b)。通过两组语音和语言治疗师评估具有代表性的语音系统的严重程度来验证模型。第一个小组是GT-I(言语和语言治疗师小组I),由三名具有应用语言学博士学位并在言语障碍方面有经验的言语和语言治疗师组成。第二个小组,GT-II(言语和语言治疗师小组II),由三名言语和语言治疗师组成,他们拥有人类交流障碍硕士学位,并在研究实验室有过言语障碍的经验。基于该建议的障碍严重程度分类与大多数被评估的语音系统中言语和语言治疗师的评估相似。此外,提案中使用的标准由第一组言语和语言治疗师使用,并被第二组认为是适当的。最后,该建议已经证明能够定量区分输入变量(路径过程、复杂程度、音素的获得)、声音类别和独特特征的程度。这些发现表明,该建议能够充分分类语音障碍的严重程度,并为言语和语言治疗师提供有效性;因此,它是临床实践的重要参考。
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PROPOSTA DE CLASSIFICAÇÃO DA GRAVIDADE DO DESVIO FONOLÓGICO POR MEIO DA MODELAGEM FUZZY, SEGUNDO O MODELO IMPLICACIONAL DE COMPLEXIDADE DE TRAÇOS
Master's Dissertation Graduate Program in Human Communication Disorders Universidade Federal de Santa Maria, RS, Brazil. CLASSIFICATION PROPOSAL OF PHONOLOGICAL DISORDER SEVERITY USING FUZZY METHODOLOGY, ACCORDING TO THE IMPLICATIONAL MODEL OF FEATURE COMPLEXITY AUTHOR: ANA RITA BRANCALIONI ADVISOR: MÁRCIA KESKE-SOARES CO-ADVISOR: KARINE FAVERZANI MAGNAGO This study aimed at proposing a quantitative classification for phonological disorder severity based on the Implicational Model of Feature Complexity – IMFC (MOTA, 1996), considering Rangel’s adequacies (1998). Quantification of such proposal was based on the Fuzzy modeling; to do so, a Linguistic Fuzzy Model was created, developed from a system of fuzzy rules, processed in parallel, using Mamdani’s minimum inference method and the center of area defuzzification method. The Model comprehended three input variables: Path Course, Level of Complexity and Acquisition of Phonemes, described in linguistic terms to which fuzzy subsets were added (three subsets for each variable). Determination of borders followed criteria and inference based on the IMFC and on the researcher’s experience. The model output variable was the Severity Phonological Disorder Index based on four fuzzy subsets: Severe, Moderate-Severe, Moderate-Mild, and Mild. The Severity Phonological Disorder Index was calculated for all 204 deviating phonological systems included in the sample using the Fuzzy Linguistic Model, run in MATLAB fuzzy toolbox (2009b). Validation of modeling was performed by evaluating severity of a representative number of phonological systems by two groups of speech and language therapists. The first, GT-I (Group of Speech and Language Therapists I), was comprised of three speech and language therapists with a doctorate in applied linguistics and experienced in disordered speech. The second, GT-II (Group of Speech and Language Therapists II), was comprised of three speech and language therapists with a master’s degree in human communication disorders and experienced in disordered speech at a research laboratory. Classification of disorder severity based on the proposal was similar to that assessed by the speech and language therapists in most phonological systems under evaluation. In addition, the criteria used in the proposal were used by Group I speech and language therapists and were considered adequate by Group II. Finally, the proposal has shown to be able to quantitatively differentiate degrees as to input variables (path course, level of complexity, acquisition of phonemes), sound classes, and distinctive features. Such findings allowed the conclusion that the proposal is able to adequately classify phonological disorder severity and presents validity for the speech and language therapists; therefore, it is an important reference for clinical practice.
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