Abstract

Automatic inference of paralinguistic information from speech, such as age, is an important area of research with many technological applications. Speaker age estimation can help with age-appropriate curation of information content and personalized interactive experiences. However, automatic speaker age estimation in children is challenging due to the paucity of speech data representing the developmental spectrum, and the large signal variability including within a given age group. Most prior approaches in child speaker age estimation adopt methods directly drawn from research on adult speech. In this paper, we propose a novel technique that exploits temporal variability present in children's speech for estimation of children's age. We focus on phone durations as biomarker of children's age. Phone duration distributions are derived by forced-aligning children's speech with transcripts. Regression models are …

Metadata

publication

The Journal of the Acoustical Society of America 152 (5), 3000-3009, 2022

year

2022

publication date

2022/11/1

authors

Prashanth Gurunath Shivakumar, Somer Bishop, Catherine Lord, Shrikanth Narayanan

link

https://pubs.aip.org/asa/jasa/article/152/5/3000/2840136

resource_link

https://arxiv.org/pdf/2109.01568

journal

The Journal of the Acoustical Society of America

volume

152

issue

5

pages

3000-3009

publisher

AIP Publishing