Unsupervised Pattern Discovery from Thematic Speech Archives Based on Multilingual Bottleneck Features
Refereed conference paper presented and published in conference proceedings

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AbstractThe present study tackles the problem of automatically discovering spoken keywords from untranscribed audio archives without requiring word-by-word speech transcription by automatic speech recognition (ASR) technology. The problem is of practical significance in many applications of speech analytics, including those concerning low-resource languages, and large amount of multilingual and multi-genre data. We propose a two-stage approach, which comprises unsupervised acoustic modeling and decoding, followed by pattern mining in acoustic unit sequences. The whole process starts by deriving and modeling a set of subword-level speech units with untranscribed data. With the unsupervisedly trained acoustic models, a given audio archive is represented by a pseudo transcription, from which
spoken keywords can be discovered by string mining algorithms. For unsupervised acoustic modeling, a deep neural network trained by multilingual speech corpora is used to generate speech segmentation and compute bottleneck features for segment clustering.Experimental results show that the proposed system is able to effectively extract topic-related words and phrases from the lecture recordings on MIT OpenCourseWare.
All Author(s) ListMan-Ling Sung, Siyuan Feng, Tan Lee
Name of ConferenceAPSIPA Annual Summit and Conference 2018
Start Date of Conference12/11/2018
End Date of Conference15/11/2018
Place of ConferenceHonolulu
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of APSIPA ASC 2018
Place of PublicationHonolulu
Pages1448 - 1455
LanguagesEnglish-United States
KeywordsZero-resource speech technology, unsupervised speech modeling, acoustic segment model, string mining

Last updated on 2018-19-12 at 16:41