Pattern Discovery in Continuous Speech Using Block Diagonal Infinite HMM
2014 (English)Conference paper (Refereed)
We propose the application of a recently introduced inference method, the Block Diagonal Infinite Hidden Markov Model (BDiHMM), to the problem of learning the topology of a Hidden Markov Model (HMM) from continuous speech in an unsupervised way. We test the method on the TiDigits continuous digit database and analyse the emerging patterns corresponding to the blocks of states inferred by the model. We show how the complexity of these patterns increases with the amount of observations and number of speakers. We also show that the patterns correspond to sub-word units that constitute stable and discriminative representations of the words contained in the speech material.
Place, publisher, year, edition, pages
2014. 3719-3723 p.
, International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Computer Science Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:kth:diva-158153DOI: 10.1109/ICASSP.2014.6854296ISI: 000343655303152ScopusID: 2-s2.0-84905262865ISBN: 978-1-4799-2893-4OAI: oai:DiVA.org:kth-158153DiVA: diva2:774991
2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014; Florence; Italy; 4 May 2014 through 9 May 2014
QC 201501212014-12-302014-12-302015-01-21Bibliographically approved