The speech of the individuals with cleft palate (CP) is generally characterized by the presence of abnormal nasal resonances during the production of voiced sounds, primarily in vowels, and is called hypernasality. Hypernasality is present in more than 50% of the individuals with CP, and it often results in degraded speech, both in quality and intelligibility. The current work describes the signal processing based enhancement of CP speech, where specifically hypernasal speech modification is addressed. The hypernasal speech’s residual and vocal tract system characteristics are analyzed using an extended weighted linear prediction (XLP) method. The enhancement is performed for three different variants: XLP residual weighting in the time domain, Gaussian mixture model-based spectral conversion in the frequency domain, and combined modification of the XLP residual and vocal tract system characteristics. The modified hypernasal speech achieved by the proposed method is evaluated using different objective and subjective measures for the vowel /a/, /i/, and /u/. The evaluation results indicate that the combination of XLP residual and vocal tract system characteristics modification yields better results than XLP residual or vocal tract system characteristics modification alone.

具有left裂（CP）的个体的语音通常特征在于主要在元音中产生浊音期间出现异常的鼻腔共鸣，这被称为鼻亢进。鼻塞症存在于超过50％的CP患者中，通常会导致语言质量和清晰度下降。当前的工作描述了基于语音处理的CP语音增强，其中专门针对鼻音语音修改。使用扩展加权线性预测（XLP）方法分析了鼻上语音的残留和声道系统特征。针对三种不同的变体进行了增强：时域中的XLP残留权重，频域中基于高斯混合模型的频谱转换，以及对XLP残余和声道系统特征的组合修改。通过对元音/ a /，/ i /和/ u /使用不同的主观和主观评估方法来评估通过所提出的方法获得的经修饰的鼻音。评估结果表明，与仅对XLP残余或声道系统特性进行修改相比，将XLP残余和声道系统特性进行修改可获得更好的结果。