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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국인터넷방송통신학회 International journal of advanced smart convergence International journal of advanced smart convergence 제7권 제2호
- 발행연도
- 2018.1
- 수록면
- 33 - 37 (5page)
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초록· 키워드
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In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.
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참고문헌 (14)
참고문헌 신청
Johnson, S.E. / 1998 / Speaker clustering using direct maximisation of the MLLR-adapted likelihood / Fifth International Conference on Spoken Language Processing
Solomonoff, A. / 1998 / Clustering speakers by their voices / Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on 2:757~760
Siu, M. H. / 2014 / Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery / Computer Speech & Language 28(1):210~223
Siu, M. H. / 2010 / Improved topic classification and keyword discovery using an HMM-based speech recognizer trained without supervision / Eleventh Annual Conference of the International Speech Communication Association
Le, V.B. / 2009 / Automatic speech recognition for under-resourced languages: application to Vietnamese language / IEEE Transactions on Audio, Speech, and Language Processing 17(8):1471~1482
Solomonoff, A. / 1998 / Clustering speakers by their voices / Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on 2:757~760
Siu, M. H. / 2014 / Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery / Computer Speech & Language 28(1):210~223
Siu, M. H. / 2010 / Improved topic classification and keyword discovery using an HMM-based speech recognizer trained without supervision / Eleventh Annual Conference of the International Speech Communication Association
Le, V.B. / 2009 / Automatic speech recognition for under-resourced languages: application to Vietnamese language / IEEE Transactions on Audio, Speech, and Language Processing 17(8):1471~1482
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