Nebula: F0 Estimation and Voicing Detection by Modeling the Statistical Properties of Feature Extractors

• Main Author: Hua, Kanru
• Format: Report
• Language: unknown
• Published: arXiv 2017
• Subjects: Audio and Speech Processing eess.AS; Sound cs.SD; FOS Electrical engineering, electronic engineering, information engineering; FOS Computer and information sciences; Arctic
• Online Access: https://dx.doi.org/10.48550/arxiv.1710.11317; https://arxiv.org/abs/1710.11317

A F0 and voicing status estimation algorithm for high quality speech analysis/synthesis is proposed. This problem is approached from a different perspective that models the behavior of feature extractors under noise, instead of directly modeling speech signals. Under time-frequency locality assumptions, the joint distribution of extracted features and target F0 can be characterized by training a bank of Gaussian mixture models (GMM) on artificial data generated from Monte-Carlo simulations. The trained GMMs can then be used to generate a set of conditional distributions on the predicted F0, which are then combined and post-processed by Viterbi algorithm to give a final F0 trajectory. Evaluation on CSTR and CMU Arctic speech databases shows that the proposed method, trained on fully synthetic data, achieves lower gross error rates than state-of-the-art methods. : To be presented at Interspeech 2018