Unsupervised spoken term detection with acoustic segment model
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員
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摘要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. © 2011 IEEE.
著者Wang H., Lee T., Leung C.-C.
會議名稱14th Annual International Conference on Speech Database and Assessments, Oriental COCOSDA 2011
會議開始日26.10.2011
會議完結日28.10.2011
會議地點Hsinchu
會議國家/地區台灣
詳細描述organized by National Chiao Tung University,
出版年份2011
月份12
日期20
頁次106 - 111
國際標準書號9781457709319
語言英式英語
關鍵詞Acoustic Segment Model, Posteriorgram, Query-by-Example, Unsupervised Spoken Term Detection

上次更新時間 2021-14-01 於 00:24