Abstract
We propose a noise-robust continuous speech recognition (CSR) method for recognition. In model building, we extract the novel feature vector by using running spectrum analysis (RSA) and dynamic range adjustment (DRA) methods. DRA adjusts the dynamic range on MFCC modulation spectrum domain (MSD). In recognition, the algorithm automatically divides the continuous speech into short sentences and blocks, then we use DRA based on the blocks. The proposed algorithm efficiency is studied for clean and noisy environment. In our experiments, all HMMs have been trained by using the Japanese newspaper article sentence (JNAS) database. The average recognition rate improves under various types of noise and SNR conditions.
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 callSimilar Papers
Disclaimer: 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.
