论文标题
使用准封闭的相位前向后线性预测分析和深神经网络的共振体跟踪
Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
论文作者
论文摘要
在本研究中，通过使用基于动态编程（DP）和深神经网（DNNS）的跟踪器来研究共振剂跟踪。使用DP方法，首先比较了六种共振体估计方法。这六种方法包括线性预测（LP）算法，加权LP算法和最近开发的准封闭相位前反复打（QCP-FB）方法。 QCP-FB在比较中提供了最佳性能。因此，提出了一种新型的实力跟踪方法，该方法结合了基于QCP-FB的深度学习和信号处理的好处。在这种方法中，使用QCP-FB从同一框架计算出的全极光谱的峰来完善语音框架中基于DNN的跟踪器预测的实扣。结果表明，与参考赋形剂跟踪器相比，最低三义剂的检测率和估计误差的表现更好。例如，与流行的波浪外相比，提出的跟踪器分别在最低三个实力的估计误差中降低了29％，48％和35％。
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation methods were first compared. The six methods include linear prediction (LP) algorithms, weighted LP algorithms and the recently developed quasi-closed phase forward-backward (QCP-FB) method. QCP-FB gave the best performance in the comparison. Therefore, a novel formant tracking approach, which combines benefits of deep learning and signal processing based on QCP-FB, was proposed. In this approach, the formants predicted by a DNN-based tracker from a speech frame are refined using the peaks of the all-pole spectrum computed by QCP-FB from the same frame. Results show that the proposed DNN-based tracker performed better both in detection rate and estimation error for the lowest three formants compared to reference formant trackers. Compared to the popular Wavesurfer, for example, the proposed tracker gave a reduction of 29%, 48% and 35% in the estimation error for the lowest three formants, respectively.
