The detection of voiced speech, the estimation of the fundamental frequency, and the tracking of pitch values over time are crucial subtasks for a variety of speech processing techniques. Many different algorithms have been developed for each of the three subtasks. We present a new algorithm that integrates the three subtasks into a single procedure. The algorithm can be applied to pre-recorded speech utterances in the presence of considerable amounts of background noise. We combine a collection of standard metrics, such as the zero-crossing rate, for example, to formulate an unsupervised voicing classifier. The estimation of pitch values is accomplished with a hybrid autocorrelation-based technique. We propose a forward-backward Kalman filter to smooth the estimated pitch contour. In experiments, we are able to show that the proposed method compares favorably with current, state-of-the-art pitch detection algorithms.

提出了一种新的算法来将有声语音的检测、基频估计和音高跟踪三个子任务集成成一个单一的过程，并通过使用标准度量标准的组合提出了一个无监督语音分类器来实现音素检测。音高值的估计采用混合自相关技术，通过引入前向-后向Kalman滤波器来平滑音高轮廓。本研究在实验中表明，所提出的方法与当前最先进的音高检测算法相比具有明显的优势。

前向-后向卡尔曼滤波无监督分类语音和音高追踪