NDLI: Unsupervised spoken term detection with spoken queries by multi-level acoustic patterns with varying model granularity

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Tao Chung Chun-an Chan Lin-shan Lee
Copyright Year	2014
Description	Author affiliation: Grad. Inst. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Cheng-Tao Chung; Lin-shan Lee) \|\| Grad. Inst. of Commun. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Chun-an Chan)
Abstract	This paper presents a new approach for unsupervised Spoken Term Detection with spoken queries using multiple sets of acoustic patterns automatically discovered from the target corpus. The different pattern HMM configurations(number of states per model, number of distinct models, number of Gaussians per state)form a three-dimensional model granularity space. Different sets of acoustic patterns automatically discovered on different points properly distributed over this three-dimensional space are complementary to one another, thus can jointly capture the characteristics of the spoken terms. By representing the spoken content and spoken query as sequences of acoustic patterns, a series of approaches for matching the pattern index sequences while considering the signal variations are developed. In this way, not only the on-line computation load can be reduced, but the signal distributions caused by different speakers and acoustic conditions can be reasonably taken care of. The results indicate that this approach significantly outperformed the unsupervised feature-based DTW baseline by 16.16% in mean average precision on the TIMIT corpus.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	7814
Ending Page	7818
File Size	344085
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6855121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustics Hidden Markov models Speech Training Conferences Speech processing spoken term detection zero resource speech recognition unsupervised learning dynamic time warping hidden Markov models
Content Type	Text
Resource Type	Article

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