Abstract
Gender detection from running speech is a very important objective to improve efficiency in tasks as speech or speaker recognition, among others. Traditionally gender detection has been focused on fundamental frequency (f0) and cepstral features derived from voiced segments of speech. The methodology presented here discards f0 as a valid feature because its estimation is complicate, or even impossible in unvoiced fragments, and its relevance in emotional speech or in strongly prosodic speech is not reliable. The approach followed consists in obtaining uncorrelated glottal and vocal tract components which are parameterized as mel-frequency coefficients. K-fold and cross-validation using QDA and GMM classifiers showed detection rates as large as 99.77 in a gender-balanced database of running speech from 340 speakers.
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Muñoz-Mulas, C., Martínez-Olalla, R., Gómez-Vilda, P., Álvarez-Marquina, A., Mazaira-Fernández, L.M. (2013). Gender Detection in Running Speech from Glottal and Vocal Tract Correlates. 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_4
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DOI: https://doi.org/10.1007/978-3-642-38847-7_4
Publisher Name: Springer, Berlin, Heidelberg
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