Blind Speech Segmentation using Spectrogram Image-based Features and Mel Cepstral Coefficients

This paper introduces a novel method for blind speech segmentation at a phone level based on image processing. We consider the spectrogram of the waveform of an utterance as an image and hypothesize that its striping defects, i.e. discontinuities, appear due to phone boundaries. Using a simple image destriping algorithm these discontinuities are found. To discover phone transitions which are not as salient in the image, we compute spectral changes derived from the time evolution of Mel cepstral parametrisation of speech. These so called image-based and acoustic features are then combined to form a mixed probability function, whose values indicate the likelihood of a phone boundary being located at the corresponding time frame. The method is completely unsupervised and achieves an accuracy of 75.59% at a -3.26% over segmentation rate, yielding an F-measure of 0.76 and an 0.80 R-value on the TIMIT dataset.