Word Classification in Children Speech using Scattering Transform

This project was conducted within the frame of a Magneton with the company LinguisTech. In this project we explored different speech recognition methods and tested them on speech recordings of children. Particularly, we examined the benefits of using the Scattering transform as a feature extraction method using different known classification algorithms such as GMM and SVM. We compared the performance of the features from the Scattering transform to the features of the MFCC which are known in the literature as efficient audio descriptors. The Scattering coefficients are a general case of the MFCC coefficients and are characterized by their stability to numerous signal deformations which allows successful classification, as was demonstrated in other tasks, such as image texture and musical genre classification.

Using an SVM classifier, the results we acquired with the features of the Scattering transform are not as successful as those acquired within the Magneton based on the MFCC features, but successful enough to indicate an applicable further research.

Word Classification in Children Speech using Scattering Transform

 

Word Classification in Children Speech using Scattering Transform
Word Classification in Children Speech using Scattering Transform

 

Collaboration:
linguistech
linguistech