Discrimination of speech and non-linguistic vocalizations by Non-Negative Matrix Factorization

We introduce features based on Non-Negative Matrix Factorization (NMF) for discrimination of speech and non-linguistic vocalizations such as laughter or breathing, which is a crucial task in recognition of spontaneous speech. NMF has been successfully used in speech-related tasks such as de-noising and speaker separation. While existing approaches use it as a preprocessing step for conventional speech recognizers, we aim at directly classifying the output of the NMF algorithm. To this end, we propose a feature extraction procedure based on a supervised variant of NMF, considering two different algorithms. Applying our approach to a spontaneous speech corpus, we show that addition of NMF features to an MFCC-based classifier increases mean recall of speech and non-linguistic vocalizations by over 2.5% absolute, and particularly recall of laughter by 6.6% absolute. The improvement is significant at a level of 0.4 %.