Unsupervised Segmentation of Speech Signals Using Kernel-Gram Matrices

Abstract

The objective of this paper is to develop an unsupervised method for segmentation of speech signals into phoneme-like units. The proposed algorithm is based on the observation that the feature vectors from the same segment exhibit higher degree of similarity than the feature vectors across the segments. The kernel-Gram matrix of an utterance is formed by computing the similarity between every pair of feature vectors in the Gaussian kernel space. The kernel-Gram matrix consists of square patches, along with the principle diagonal, corresponding to different phoneme-like segments in the speech signal. It detects the number of segments, as well as their boundaries automatically. The proposed approach does not assume any information about input utterances like exact distribution of segment length or correct number of segments in an utterance. The proposed method out-performs the state-of-the-art blind segmentation algorithms on Zero Resource 2015 databases and TIMIT database.