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Automatic Modeling of Acoustic Perception of Breathiness in Pathological Voices

机译:病理性声音中呼吸声的自动感知的自动建模

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This paper revisits the modeling of acoustic perceptions of breathy voice (BV) quality for automatic assessment of perturbations in pathologic speech. Several acoustic measures related with the signal periodicity, harmonic components, and aspiration noise are studied to predict breathiness judgments performed on sustained vowel phonations. A novel comprehensive automatic measure is proposed that provides the highest correlation index (88.5%) with breathiness judgment performed by trained specialists on simulated and recorded utterances. The new measure reveals the most relevant aspects of BV quality and provides a vehicle to obtain reliable objectives judgments of such speech perturbation.
机译:本文重新审视了对呼吸语音(BV)质量的声学感知的建模,以自动评估病理性语音中的扰动。研究了几种与信号周期,谐波分量和吸入噪声相关的声学测量方法,以预测对持续元音发声进行的呼吸判断。提出了一种新颖的综合自动测量方法,该方法可提供最高的相关指数(88.5%),并且由受过训练的专家对模拟和记录的语音进行呼吸判断。这项新措施揭示了BV质量的最相关方面,并提供了一种工具来获得对此类语音干扰的可靠目标判断。

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