Abstract
In this paper we present a robust feature extractor that includes the use of a smoothed nonlinear energy operator (SNEO)-based amplitude modulation features for a large vocabulary continuous speech recognition (LVCSR) task. SNEO estimates the energy required to produce the AM-FM signal, and then the estimated energy is separated into its amplitude and frequency components using an energy separation algorithm (ESA). Similar to the PNCC (Power Normalized Cepstral Coefficients) front-end, a medium duration power bias subtraction (MDPBS) is used to enhance the AM power spectrum. The performance of the proposed feature extractor is evaluated, in the context of speech recognition, on the AURORA-4 corpus, which represents additive noise and channel mismatch conditions. The ETSI advanced front-end (ETSI-AFE),power normalized cepstral coefficients (PNCC), Cochlear filterbank cepstral coefficients (CFCC) and conventional MFCC and PLP features are used for comparison purposes. Experimental speech recognition results on the AURORA-4 task depict that the proposed method is robust against both additive and different microphone channel environments.
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.
