Improving Children Speech Recognition through Feature Learning from Raw Speech Signal

S. Pavankumar Dubagunta; Selen Hande Kabil; Mathew Magimai-Doss

ICASSP 2019accepted0 citations

Improving Children Speech Recognition through Feature Learning from Raw Speech Signal

S. Pavankumar Dubagunta, Selen Hande Kabil, Mathew Magimai-Doss

Abstract

Children speech recognition based on short-term spectral features is a challenging task. One of the reasons is that children speech has high fundamental frequency that is comparable to formant frequency values. Furthermore, as children grow, their vocal apparatus also undergoes changes. This presents difficulties in extracting standard short-term spectral-based features reliably for speech recognition. In recent years, novel acoustic modeling methods have emerged that learn both the feature and phone classifier in an end-to-end manner from the raw speech signal. Through an investigation on PF-STAR corpus we show that children speech recognition can be improved using end-to-end acoustic modeling methods.

BibTeX

@inproceedings{icassp2019_improvingchildre,
  title = {Improving Children Speech Recognition through Feature Learning from Raw Speech Signal},
  author = {S. Pavankumar Dubagunta and Selen Hande Kabil and Mathew Magimai-Doss},
  booktitle = {ICASSP 2019},
  year = {2019}
}