ISCA Archive Interspeech 2014
ISCA Archive Interspeech 2014

Identification of age-group from children's speech by computers and humans

Saeid Safavi, Martin Russell, Peter Jančovič

This paper presents results on age-group identification (Age-ID) for children's speech, using the OGI Kids corpus and GMM-UBM, GMM-SVM and i-vector systems. Regions of the spectrum containing important age information for children are identified by conducting Age-ID experiments over 21 frequency sub-bands. Results show that the frequencies above 5.5 kHz are least useful for Age-ID. The effect of using gender-independent and gender-dependent age-group modelling is explored. The GMM-UBM and i-vector systems considerably outperform the GMM-SVM system. The best Age-ID performance of 85.77% is obtained by the i-vector system applied to band-limited speech to 5.5 kHz. Experiments on human Age-ID were also conducted and the results show that the humans do not achieve the performance of the machine.


doi: 10.21437/Interspeech.2014-61

Cite as: Safavi, S., Russell, M., Jančovič, P. (2014) Identification of age-group from children's speech by computers and humans. Proc. Interspeech 2014, 243-247, doi: 10.21437/Interspeech.2014-61

@inproceedings{safavi14_interspeech,
  author={Saeid Safavi and Martin Russell and Peter Jančovič},
  title={{Identification of age-group from children's speech by computers and humans}},
  year=2014,
  booktitle={Proc. Interspeech 2014},
  pages={243--247},
  doi={10.21437/Interspeech.2014-61},
  issn={2308-457X}
}