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.
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Alam, M.J., Kenny, P., O’Shaughnessy, D. (2013). Smoothed Nonlinear Energy Operator-Based Amplitude Modulation Features for Robust Speech Recognition. In: Drugman, T., Dutoit, T. (eds) Advances in Nonlinear Speech Processing. NOLISP 2013. Lecture Notes in Computer Science(), vol 7911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38847-7_22
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DOI: https://doi.org/10.1007/978-3-642-38847-7_22
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