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Vowel Onset Point Based Screening of Misarticulated Stops in Cleft Lip and Palate Speech

机译:基于元素的爆破唇部和口感言论的筛选点的元音

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The presence of velopharyngeal dysfunction, dental occlusion, and mislearned articulation in individuals with cleft lip and palate (CLP) results in the production of misarticulated stop consonants. The present work considers vowel onset points (VOPs) as the anchor points, around which the consonant-vowel (CV) transition regions are segmented to analyze the difference between normal and misarticulated stops. VOPs are located using an epoch-synchronously computed feature called maximum weighted inner product. Spectro-temporal dynamics of CV transitions anchored around VOP are analyzed using two-dimensional discrete cosine transform (2D-DCT) coefficients, where 2D-DCT coefficients are derived from single pole filter (SPF) based time-frequency representation. The SPF-based 2D-DCT coefficients are used to train a support vector machine for the classification of normal and misarticulated stops, where the class of misarticulated stops includes weak, nasalized, palatal, velar, pharyngeal, glottal, and devoicing errors produced by CLP speakers. The performance of the proposed VOP detection algorithm is evaluated on a database containing CV units of normal and misarticulated stops, and the results are compared with the state-of-the-art VOP detection methods. The classification results obtained for the proposed SPF-based 2D-DCT coefficients are compared with the short-time Fourier transform-based 2D-DCT coefficients and Mel-frequency cepstral coefficients. Further, the performance of the proposed system is compared with the hidden Markov model-based goodness of pronunciation approach.
机译:具有唇腭裂和腭裂(CLP)的个体的腭核功能障碍,牙齿闭塞和错误的关节导致梭氏凝固的粘性辅助。本工作将元音发作点(VOPS)视为锚点,周围辅音元音(CV)过渡区域被分割以分析正常和夹颗粒间隔之间的差异。 vops位于使用epoch同步计算功能,称为最大加权内部产品。使用二维离散余弦变换(2D-DCT)系数来分析围绕VOP锚定的CV转换的光谱 - 时间动态,其中2D-DCT系数来自基于单极滤波器(SPF)的时频表示。基于SPF的2D-DCT系数用于训练用于正常和梭形止挡的分类的支持向量机,其中梭氏槽等包括CLP产生的弱,鼻的,腭,VelAR,咽部,光泽和开发误差发言者。所提出的VOP检测算法的性能在包含正常和截止的停止的CV单位的数据库上进行评估,并将结果与​​最先进的VOP检测方法进行比较。将所提出的基于SPF的2D-DCT系数获得的分类结果与基于短时傅里叶变换的2D-DCT系数和熔融频率谱系数进行比较。此外,将所提出的系统的性能与基于隐马尔可夫模型的发音方法进行比较。

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