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- W2137997377 abstract "MFCC (Mel Frequency Cepstral Coefficients) and PLP (Perceptual linear prediction coefficients) or RASTA-PLP have demonstrated good results whether when they are used in combination with prosodic features as suprasegmental (long-term) information or when used stand-alone as segmental (short-time) information. MFCC and PLP feature parameterization aims to represent the speech parameters in a way similar to how sound is perceived by humans. However, MFCC and PLP are usually computed from a Hamming-windowed periodogram spectrum estimate that is characterized by large variance. In this paper we study the effect of averaging spectral estimates obtained using a set of orthogonal tapers (windows) on emotion recognition performance. The multitaper MFCC and PLP are examined separately as short-time information vectors modeled using Gaussian mixture models (GMMs). When tested on the FAU AIBO spontaneous emotion corpus, a relative improvement ranging from 2.2% to 3.9% for both MFCC and PLP systems is achieved by multiple windowed spectral features compared to single windowed ones." @default.
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- W2137997377 date "2013-05-01" @default.
- W2137997377 modified "2023-09-25" @default.
- W2137997377 title "Multiple windowed spectral features for emotion recognition" @default.
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- W2137997377 doi "https://doi.org/10.1109/icassp.2013.6639126" @default.
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