Learning Representations for Nonspeech Audio Events through Their Similarities to Speech Patterns

The human auditory system is very well matched to both human speech and environmental sounds. Therefore, the question arises whether human speech material may provide useful information for training systems for analyzing nonspeech audio signals, e.g., in a classification task. In order to answer this question, we consider speech patterns as basic acoustic concepts, which embody and represent the target nonspeech signal. To find out how similar the nonspeech signal is to speech, we classify it with a classifier trained on the speech patterns and use the classification posteriors to represent the closeness to the speech bases. The speech similarities are finally employed as a descriptor to represent the target signal. We further show that a better descriptor can be obtained by learning to organize the speech categories hierarchically with a tree structure. Furthermore, these descriptors are generic. That is, once the speech classifier has been learned, it can be employed as a feature extractor for different datasets without retraining. Lastly, we propose an algorithm to select a sufficient subset, which provides an approximate representation capability of the entire set of available speech patterns. We conduct experiments for the application of audio event analysis. Phone triplets from the TIMIT dataset were used as speech patterns to learn the descriptors for audio events of three different datasets with different complexity, including UPC-TALP, Freiburg-106, and NAR. The experimental results on the event classification task show that a good performance can be easily obtained even if a simple linear classifier is used. Furthermore, fusion of the learned descriptors as an additional source leads to state-of-the-art performance on all the three target datasets.