Funding Information: Funding Stephanie Balzer: National Science Foundation Award No. CCF-1718267. Bernardo Toninho: FCT/MCTES grant NOVALINCS/BASE UIDB/04516/2020. Publisher Copyright: © Ruo Fei Chen, Stephanie Balzer, and Bernardo Toninho; licensed under Creative Commons License CC-BY 4.0

In this paper, we present a novel post-processing scheme to improve autoregressive (AR) speech parameter estimation in noise. The proposed technique exploits temporal correlation between dynamic all-pole parameters to capture natural speech evolution. To achieve this, a Kalman tracking scheme is proposed to track line spectrum frequency (LSF) trajectories with system parameter learned directly from processed online data. To facilitate the online system identification, a heuristic approach is initially proposed to preliminarily remove “musical tones” caused by conventional frame-based methods in LSF domain. Through performance evaluation based on a study of spectrogram and objective measures, it is demonstrated that the proposed post-processing scheme successfully restores natural and smooth evolution of speech dynamics, and in the meantime, effectively removes processing artifacts caused by conventional methods in various conditions.

In this paper, we propose two block-based clustering and identification algorithms that contribute to robust estimation of autoregressive (AR) speech parameters in noisy environments. Motivated by the fact that the evolution pattern of speech dynamics could be an observable feature that are retained in a series of noisy observations, a dynamic state tracking scheme based on Kalman filter is incorporated to utilize this additional trajectory information in block-based AR codebook design. The proposed algorithm is devised in a sense that AR blocks with similar clean line spectrum frequency trajectories as well as noisy-to-clean mappings are clustered offline and identified online. It is compared with conventional vector quantization based approaches that directly minimize a distortion between AR parameters. Through objective assessments based on mean square error and log-spectral distance, it is demonstrated that the proposed algorithm achieves significant improvement over conventional methods in various conditions.

In this work, we present a model-based approach to enhance noisy speech using an analysis-synthesis framework. Target speech is reconstructed with model parameters estimated from noisy observations. In particular, spectral envelope is estimated by tracking its temporal trajectories in order to improve the noise-distorted short-time spectral amplitude. Initially, we propose an analysis-synthesis framework for speech enhancement based on harmonic noise model (HNM). Acoustic parameters such as pitch, spectral envelope, and spectral gain are extracted from HNM analysis. Spectral envelope estimation is improved by tracking its line spectrum frequency trajectories through Kalman filtering. System identification of Kalman filter is achieved via a combined design of codebook mapping scheme and maximum-likelihood estimator with parallel training data. Complete system design and experimental validations are given in details. Through performance evaluation based on a study of spectrogram, objective measures and a subjective listening test, it is demonstrated that the proposed approach achieves significant improvement over conventional methods in various conditions. A distinct advantage of the proposed method is that it successfully tackles the “musical tones” problem.

This paper presents a Kalman tracking approach to re-estimate clean spectral amplitude from noisy speech spectrum for re-synthesis based speech enhancement. The motivation of using Kalman filter and training is to exploit the temporal correlation between speech dynamics and to include prior knowledge of speech to improve the model parameter estimation in harmonic noise model (HNM) based speech enhancement system. The re-estimated harmonic amplitude is fitted into an analysis-synthesis framework to accomplish a more accurate HNM based re-synthesis. Objective evaluation results show the proposed method achieves significant improvement over various classical short-time spectral amplitude (STSA) based methods, especially in low signal-to-noise ratio environments.