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Robustness Improvement of Hypernasal Speech Detection by Acoustic Analysis and the Rademacher Complexity Model

机译：声学分析和Rademacher复杂度模型提高鼻音检测的鲁棒性

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People with a defective velopharyngeal mechanism speak with abnormal nasal resonance (hypernasal speech). Voice analysis methods for hypernasality detection commonly use vowels and nasalized vowels. However to obtain a more general assessment of this abnormality it is necessary to analyze stops and fricatives. This study describes a method with high generalization capability for hypernasality detection analyzing unvoiced Spanish stop consonants. The importance of phoneme-by-phoneme analysis is shown, in contrast with whole word parametrization which includes irrelevant segments from the classification point of view. Parameters that correlate the imprints of Velopharyngeal Incompetence (VPI) over voiceless stop consonants were used in the feature estimation stage. Classification was carried out using a Support Vector Machine (SVM), including the Rademacher complexity model with the aim of increasing the generalization capability. Performances of 95.2% and 92.7% were obtained in the processing and verification stages for a repeated cross-validation classifier evaluation.
机译：咽喉机制有缺陷的人说话时鼻腔反响异常（鼻腔言语）。用于鼻音检测的语音分析方法通常使用元音和鼻音元音。但是，要获得对该异常的更一般的评估，必须分析止动件和摩擦件。这项研究描述了一种用于鼻音检测的高泛化能力的方法，用于分析清音西班牙停止辅音。显示了逐音素分析的重要性，与从分类角度来看包括无关片段的整个单词参数化形成对比。在特征估计阶段中，使用了与无声终止辅音上的咽喉功能不全（VPI）的印记相关的参数。使用支持向量机（SVM）进行分类，包括Rademacher复杂度模型，目的是提高泛化能力。在处理和验证阶段，针对重复的交叉验证分类器评估，获得了95.2％和92.7％的性能。

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