Recent work has explored deep architectures for learning acoustic features in an unsupervised or weakly-supervised way for phone recognition. Here we investigate the role of the input features, and in particular we test whether standard mel-scaled filterbanks could be replaced by inherently richer representations, such as derived from an analytic scattering spectrum. We use a Siamese network using lexical side information similar to a well-performing architecture used in the Zero Resource Speech Challenge (2015), and show a substantial improvement when the filterbanks are replaced by scattering features, even though these features yield similar performance when tested without training. This shows that unsupervised and weakly-supervised architectures can benefit from richer features than the traditional ones.
['Neil Zeghidour', 'Gabriel Synnaeve', 'Maarten Versteegh', 'Emmanuel Dupoux']
A deep scattering spectrum — Deep Siamese network pipeline for unsupervised acoustic modeling