Unsupervised spoken term detection with acoustic segment model

Abstract

This paper describes a study on query-by-example spoken term detection (STD) using the acoustic segment modeling technique. Acoustic segment models (ASMs) are a set of hidden Markov models (HMM) that are obtained in an unsupervised manner without using any transcription information. The training of ASMs follows an iterative procedure, which consists of the steps of initial segmentation, segments labeling, and HMM parameter estimation. The ASMs are incorporated into a template-matching framework for query-by-example STD. Both the spoken query examples and the test utterances are represented by frame-level ASM posteriorgrams. Segmental dynamic time warping (DTW) is applied to match the query with the test utterance and locate the possible occurrences. The performance of the proposed approach is evaluated with different DTW local distance measures on the TIMIT and the Fisher Corpora respectively. Experimental results show that the use of ASM posteriorgrams leads to consistently better performance of detection than the conventional GMM posteriorgrams.