Abstract
In this paper, we present an effective feature normalization algorithm to improve the robustness of automatic speech recognition systems. At front-end, minimum mean square error log-spectral amplitude estimation speech enhancement is adopted to suppress noise from noisy speech. Then, at back-end, the histogram equalization feature normalization is used to deal with the residual mismatch between enhanced speech and clean speech. We have evaluated recognition performance under noisy environments using NOISEX-92 database and recorded speech signals in continuous speech recognition task. Experimental results show that our approach exhibits considerable improvements in the degraded environment.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.