A Data-Driven Model Parameter Compensation Method for Noise-Robust Speech Recognition
Y. CHUNG · IEICE Transactions on Information and Systems · 2005
A data-driven approach that compensates the HMM parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional methods such as the PMC, the various statistical information necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared with the PMC for the noisy speech recognition.