Identification of age-group from children's speech by computers and humans
نویسندگان
چکیده
This paper presents results on age-group identification (AgeID) 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 genderindependent 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 AgeID were also conducted and the results show that the humans do not achieve the performance of the machine.
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