Abstract
Human recognition of isolated vowels is quite robust considering intra and inter-speaker variability. Automatic recognition techniques typically exhibit poor performances, notably in the case of female or child speech because a higher fundamental frequency (F0) generates a sparser sampling of the magnitude spectrum.
In this paper we extend previous results on a perceptually motivated concept of vowel recognition that is based on Perceptual Spectral Clusters (PSC) of harmonic partials. We study the effect of normalizing relevant PSC features by F0 taking as a reference the recognition performance of static features derived from either Linear Prediction (LP) analysis or Mel-Frequency Cepstral Coefficients (MFCC), and using the Mahalanobis distance on a data base of five natural Portuguese vowel sounds uttered by 44 speakers. Test results reveal that the recognition performance of F0-normalized PSC features increases approaching that of MFCC coefficients. These results are significant as PSC related features are amenable to concurrent vowel identification while LP or MFCC-related features are not.
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Ferreira, A. (2009). Automatic Recognition of Isolated Vowels Using F0-Normalized Harmonic Features. In: Filipe, J., Obaidat, M.S. (eds) e-Business and Telecommunications. ICETE 2008. Communications in Computer and Information Science, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05197-5_22
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DOI: https://doi.org/10.1007/978-3-642-05197-5_22
Publisher Name: Springer, Berlin, Heidelberg
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