A robust unsupervised pattern discovery and clustering of speech signals

摘要

In this paper, a novel approach to unsupervised pattern discovery for speech signals is proposed. The proposed work deviates from the standard speech recognition task, and aims to cluster the speech utterances based on the vocabulary of a broad topic. It attempts to discover the matched sequence of phonetic units by making use of the repeated patterns between the speech signals. Identification of matched sequence of phonetic patterns helps in clustering the speech signals, automatically. The proposed approach uses the posterior features derived from Gaussian mixture model (GMM) to find the repeated structure between the speech signals. Image processing techniques are used to identify these matched acoustic patterns. An angle histogram-based method is used to extract the desired matched keyword/phrase patterns present in a pair of speech utterances. The performance of the proposed method is evaluated on Hindi and Bengali news speech corpora using standard objective measures, and also compared with state-of-the-art techniques. The matched pairs of speech utterances obtained by the proposed method are grouped into broader classes using an appropriate clustering technique. The final clusters represent the broader classes of information such as politics, sports, and weather. (C) 2018 Elsevier B.V. All rights reserved.