A Voice Classification System for Younger Children with Applications to Content Navigation

Lowis C, Pike C, Raimond Y. A Voice Classification System for Younger Children with Applications to Content Navigation. In: AES Convention 132. Audio Engineering Society; 2012. Paper 8589. Available from: https://aes.org/publications/elibrary-page/?id=16227

@inproceedings{Lowis2012_16227,
  author = {Lowis, Christopher and Pike, Christopher and Raimond, Yves},
  title = {{A Voice Classification System for Younger Children with Applications to Content Navigation}},
  booktitle = {AES Convention 132},
  note = {Paper 8589},
  year = {2012},
  month = apr,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16227}
}

TY  - CPAPER
TI  - A Voice Classification System for Younger Children with Applications to Content Navigation
AU  - Lowis, Christopher
AU  - Pike, Christopher
AU  - Raimond, Yves
T2  - AES Convention 132
M1  - Paper 8589
PY  - 2012
DA  - 2012/04/06
UR  - https://aes.org/publications/elibrary-page/?id=16227
PB  - Audio Engineering Society
LA  - en
AB  - A speech classification system is proposed which has applications for accessibility of content for younger children. To allow a young child to access online content (where typical interfaces such as search engines or hierarchical navigation would be inappropriate) we propose a voice classification system trained to recognise a range of sounds and vocabulary typical of younger children. As an example we design a system for classifying animal noises. Acoustic features are extracted from a corpus of animal noises made by a class of young children. A Support Vector Machine is trained to classify the sounds into one of 12 corresponding animals. We investigate the precision and recall of the classifier for various classification parameters. We investigate an appropriate choice of features to extract from the audio and compare the performance when using mean Mel-frequency Cepstral Coefficients (MFCC), a single-Gaussian model fitted to the MFCCs as well as a range of temporal features. To investigate the real-world applicability of the system we pay particular attention to the difference between training a generic classifier from a collected corpus of examples and one trained to a particular voice.
ER  -