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Comparison of four approaches to age and gender recognition for telephone applications

  • Florian Metze
  • , Jitendra Ajmera
  • , Roman Englert
  • , Udo Bub
  • , Felix Burkhardt
  • , Joachim Stegmann
  • , Christian Müller
  • , Richard Huber
  • , Bernt Andrassy
  • , Josef G. Bauer
  • , Bernhard Littel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

124 Scopus citations

Abstract

This paper presents a comparative study of four different approaches to automatic age and gender classification using seven classes on a telephony speech task and also compares the results with Human performance on the same data. The automatic approaches compared are based on (1) a parallel phone recognizer, derived from an automatic language identification system; (2) a system using dynamic Bayesian networks to combine several prosodie features; (3) a system based solely on linear prediction analysis; and (4) Gaussian mixture models based on MFCCs for separate recognition of age and gender. On average, the parallel phone recognizer performs as well as Human listeners do, while loosing performance on short utterances. The system based on prosodie features however shows very little dependence on the length of the utterance.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesIV1089-IV1092
DOIs
StatePublished - 6 Aug 2007
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: 15 Apr 200720 Apr 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
ISSN (Print)1520-6149

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period15/04/0720/04/07

Keywords

  • Acoustic signal analysis
  • Age
  • Gender
  • Speaker classification
  • Speech processing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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