Joint tracking of clean speech and noise using HMMs and particle filters for robust speech recognition

We propose a dynamic joint tracking framework to monitor the clean speech signal and noise simultaneously in order to compensate the noisy features. The clean speech signal is tracked using an integrated algorithm based on both particle filters and hidden Markov models. The information available from speech tracking is used for tracking and estimating the noise parameters. The availability of dynamic noise information enhances the robustness of the algorithm in case of large fluctuations in noise. We report on experimental results obtained with the Aurora-2 connected digit recognition task, and show that the performance for the additive noise cases can be improved by 12:15% over the state-of-the-art multi-condition training if the noise mean is updated every 300 milliseconds.