Literature DB >> 26082150

Gender identification from high-pass filtered vowel segments: the use of high-frequency energy.

Jeremy J Donai1, Norman J Lass2.   

Abstract

The purpose of this study was to examine the use of high-frequency information for making gender identity judgments from high-pass filtered vowel segments produced by adult speakers. Specifically, the effect of removing lower-frequency spectral detail (i.e., F3 and below) from vowel segments via high-pass filtering was evaluated. Thirty listeners (ages 18-35) with normal hearing participated in the experiment. A within-subjects design was used to measure gender identification for six 250-ms vowel segments (/æ/, /ɪ /, /ɝ/, /ʌ/, /ɔ/, and /u/), produced by ten male and ten female speakers. The results of this experiment demonstrated that despite the removal of low-frequency spectral detail, the listeners were accurate in identifying speaker gender from the vowel segments, and did so with performance significantly above chance. The removal of low-frequency spectral detail reduced gender identification by approximately 16 % relative to unfiltered vowel segments. Classification results using linear discriminant function analyses followed the perceptual data, using spectral and temporal representations derived from the high-pass filtered segments. Cumulatively, these findings indicate that normal-hearing listeners are able to make accurate perceptual judgments regarding speaker gender from vowel segments with low-frequency spectral detail removed via high-pass filtering. Therefore, it is reasonable to suggest the presence of perceptual cues related to gender identity in the high-frequency region of naturally produced vowel signals. Implications of these findings and possible mechanisms for performing the gender identification task from high-pass filtered stimuli are discussed.

Keywords:  Bandwidth; Gender; High-frequency energy; Identification; Vowels

Mesh:

Year:  2015        PMID: 26082150     DOI: 10.3758/s13414-015-0945-y

Source DB:  PubMed          Journal:  Atten Percept Psychophys        ISSN: 1943-3921            Impact factor:   2.199


  1 in total

1.  Automated Classification of Vowel Category and Speaker Type in the High-Frequency Spectrum.

Authors:  Jeremy J Donai; Saeid Motiian; Gianfranco Doretto
Journal:  Audiol Res       Date:  2016-04-20
  1 in total

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