Unsupervised Discovery of Structured Acoustic Tokens With Applications to Spoken Term Detection

In this paper, we compare two paradigms for unsupervised discovery of structured acoustic tokens directly from speech corpora without any human annotation. The multigranular paradigm seeks to capture all available information in the corpora with multiple sets of tokens for different model granularit...

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Bibliographic Details
Published inIEEE/ACM transactions on audio, speech, and language processing Vol. 26; no. 2; pp. 394 - 405
Main Authors Chung, Cheng-Tao, Lee, Lin-Shan
Format Journal Article
LanguageEnglish
Published IEEE 01.02.2018
Subjects
Acoustics
automatic speech recognition
Hidden Markov models
Manganese
Pragmatics
Speech
Speech processing
spoken term detection
Training
unsupervised term discovery
Zero resource
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Summary:In this paper, we compare two paradigms for unsupervised discovery of structured acoustic tokens directly from speech corpora without any human annotation. The multigranular paradigm seeks to capture all available information in the corpora with multiple sets of tokens for different model granularities. The hierarchical paradigm attempts to jointly learn several levels of signal representations in a hierarchical structure. The two paradigms are unified within a theoretical framework in this paper. Query-by-example spoken term detection (QbE-STD) experiments on the query by example search on speech task dataset of MediaEval 2015 verifies the competitiveness of the acoustic tokens. The enhanced relevance score proposed in this work improves both paradigms for the task of QbE-STD. We also list results on the ABX evaluation task of the Zero Resource Challenge 2015 for comparison of the paradigms.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2017.2778948