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Automatic hypernasality grade assessment in cleft palate speech based on the spectral envelope method.

基于谱包络法的腭裂语音多鼻音等级自动评估。

  • 影响因子:1.24
  • DOI:10.1515/bmt-2018-0181
  • 作者列表:"Zhang J","Yang S","Wang X","Tang M","Yin H","He L
  • 发表时间:2020-01-28
Abstract

:Due to velopharyngeal incompetence, airflow overflows from the oral cavity to the nasal cavity, which results in hypernasality. Hypernasality greatly reduces speech intelligibility and affects the daily communication of patients with cleft palate. Accurate assessment of hypernasality grades can provide assisted diagnosis for speech-language pathologists (SLPs) in clinical settings. Utilizing a support vector machine (SVM), this paper classifies speech recordings into four grades (normal, mild, moderate and severe hypernasality) based on vocal tract characteristics. Linear prediction (LP) analysis is widely used to model the vocal tract. Glottal source information may be included in the LP-based spectrum. The stabilized weighted linear prediction (SWLP) method, which imposes the temporal weights on the closed-phase interval of the glottal cycle, is a more robust approach for modeling the vocal tract. The extended weighted linear prediction (XLP) method weights each lagged speech signal separately, which achieves a finer time scale on the spectral envelope than the SWLP method. Tested speech recordings were collected from 60 subjects with cleft palate and 20 control subjects, and included a total of 4640 Mandarin syllables. The experimental results showed that the spectral envelope of normal speech decreases faster than that of hypernasal speech in the high-frequency part. The experimental results also indicate that the SWLP- and XLP-based methods have smaller correlation coefficients between normal and hypernasal speech than the LP method. Thus, the SWLP and XLP methods have better ability to distinguish hypernasal from normal speech than the LP method. The classification accuracies of the four hypernasality grades using the SWLP and XLP methods range from 83.86% to 97.47%. The selection of the model order and the size of the weight function are also discussed in this paper.

摘要

: 由于腭咽闭合不全,气流从口腔溢出到鼻腔,导致鼻过度。鼻过度大大降低了语音清晰度,影响了腭裂患者的日常交流。准确评估多鼻症分级可为临床环境中的言语-语言病理学家 (SLPs) 提供辅助诊断。利用支持向量机 (SVM),根据声道特征,将语音记录分为四个等级 (正常、轻度、中度和重度高鼻音)。线性预测 (LP) 分析被广泛用于声带的建模。Glottal来源信息可能包含在基于LP的光谱中。稳定加权线性预测 (SWLP) 方法,对声门周期的闭合相位间隔施加时间权重,是一种更稳健的声带建模方法。扩展加权线性预测 (XLP) 方法分别对每个滞后语音信号进行加权,比SWLP方法在谱包络上实现了更精细的时间尺度。从 60 例腭裂受试者和 20 例对照受试者中收集测试的语音记录,共包括 4640 个普通话音节。实验结果表明,在高频部分,正常语音的频谱包络比高鼻语音的频谱包络降低更快。实验结果还表明,基于SWLP和XLP的方法与LP方法相比,正常和超鼻语音之间的相关系数较小。因此,SWLP和XLP方法比LP方法具有更好的区分鼻高音和正常语音的能力。使用SWLP和XLP方法的四种鼻部分级的分类精度范围为 83.86% 至 97.47%。本文还讨论了模型阶数的选择和权函数的大小。

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影响因子:0.95
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翻译标题与摘要 下载文献
影响因子:2.43
发表时间:2020-01-15
DOI:10.5435/JAAOS-D-16-00918
作者列表:["Grigoryan G","Korcek L","Eidelman M","Paley D","Nelson S"]

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