Robust block-based clustering and identification of autoregressive speech parameters based on dynamic state tracking

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.