A biologically inspired onset and offset speech segmentation approach

Abstract

A key component in the processing of speech is the division of longer input sounds into a number of smaller sections. For speech interpretation it is generally easier to classify single sections. Similarly, when processing speech for other purposes (e.g. speech filtering), it can be easier and more relevant to process individual phonemes. Here, we propose a biologically inspired speech segmentation technique that filters the speech into multiple bandpassed channels using a Gammatone filterbank, and then uses an essentially energy-based spike coding technique in order to find the onsets and offsets present in an audio signal. These onsets and offsets are then processed using leaky integrate-and-fire neurons, and the spikes from these used to determine the speech segmentation. We evaluate this new system using a quantitative evaluation metric, and the promising results of segmentation of both clean speech and speech in noise demonstrate the effectiveness of this technique.