Periodicity is an important property of speech signals. It is the basis of the signal’s fundamental frequency and the pitch of voice, which is crucial to speech communication. This paper presents a novel framework of periodicity enhancement for noisy speech. The enhancement is applied to the linear prediction residual of speech. The residual signal goes through a constant-pitch time warping process and two sequential lapped-frequency transforms, by which the periodic component is concentrated in certain transform coefficients. By emphasizing the respective transform coefficients, periodicity enhancement of noisy residual signal is achieved. The enhanced residual signal and estimated linear prediction filter parameters are used to synthesize the output speech. An adaptive algorithm is proposed for adjusting the weights for the periodic and aperiodic components. Effectiveness of the proposed approach is demonstrated via experimental evaluation. It is observed that harmonic structure of the original speech could be properly restored to improve the perceptual quality of enhanced speech.

周期性是语音信号的重要属性。它是信号基本频率和语音音调的基础，这对语音通信至关重要。本文提出了一种用于噪声语音的周期性增强的新颖框架。该增强被应用于语音的线性预测残差。残留信号经过恒定音调时间扭曲过程和两个顺序的重叠频率变换，通过这些变换，周期分量会集中在特定的变换系数中。通过强调各个变换系数，实现了噪声残留信号的周期性增强。增强的残差信号和估计的线性预测滤波器参数用于合成输出语音。提出了一种自适应算法，用于调整周期和非周期分量的权重。通过实验评估证明了该方法的有效性。观察到可以适当地恢复原始语音的谐波结构以提高增强语音的感知质量。