A Corpus-based study on the Effects of Gender on Voiceless Fricatives in American English
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This paper investigates the acoustic characteristics of English fricatives in the TIMIT corpus, with a special focus on therole of gender in rendering fricatives in American English. The TIMIT database includes 630 talkers and 2342 differentsentences, comprising over five hours of speech. Acoustic analyses are conducted in the domain of spectral and temporalproperties by treating gender as an independent factor. The results of acoustic analyses revealed that the most acousticproperties of voiceless sibilants turned out to be different between male and female speakers, but those of voicelessnon-sibilants did not show differences. A classification experiment using linear discriminant analysis (LDA) revealed that85.73% of voiceless fricatives are correctly classified. The sibilants are 88.61% correctly classified, whereas the non-sibilantsare only 57.91% correctly classified. The majority of the errors are from the misclassification of /θ/ as [f]. The averageaccuracy of gender classification is 77.67%. Most of the inaccuracy results are from the classification of female speakers innon-sibilants. The results are accounted for by resorting to biological differences as well as macro-social factors. The papercontributes to the understanding of the role of gender in a large-scale speech corpus.
Abstract
This paper investigates the acoustic characteristics of English fricatives in the TIMIT corpus, with a special focus on therole of gender in rendering fricatives in American English. The TIMIT database includes 630 talkers and 2342 differentsentences, comprising over five hours of speech. Acoustic analyses are conducted in the domain of spectral and temporalproperties by treating gender as an independent factor. The results of acoustic analyses revealed that the most acousticproperties of voiceless sibilants turned out to be different between male and female speakers, but those of voicelessnon-sibilants did not show differences. A classification experiment using linear discriminant analysis (LDA) revealed that85.73% of voiceless fricatives are correctly classified. The sibilants are 88.61% correctly classified, whereas the non-sibilantsare only 57.91% correctly classified. The majority of the errors are from the misclassification of /θ/ as [f]. The averageaccuracy of gender classification is 77.67%. Most of the inaccuracy results are from the classification of female speakers innon-sibilants. The results are accounted for by resorting to biological differences as well as macro-social factors. The papercontributes to the understanding of the role of gender in a large-scale speech corpus.
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