Abstract
We present a model-based noise compensation algorithm for robust speech recognition in nonstationary noisy environments. The effect of noise is split into a stationary part, compensated by parallel model combination, and a time varying residual. The evolution of residual noise parameters is represented by a set of state space models. The state space models are updated by Kalman prediction and the sequential maximum likelihood algorithm. Prediction of residual noise parameters from different mixtures are fused, and the fused noise parameters are used to modify the linearized likelihood score of each mixture. Noise compensation proceeds in parallel with recognition. Experimental results demonstrate that the proposed algorithm improves recognition performance in highly nonstationary environments, compared with parallel model combination alone.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
