Intermediate-State HMMs to Capture Continuously-Changing Signal Features
2011 (English)In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2011, 1828-1831 p.Conference paper (Refereed)
Traditional discrete-state HMMs are not well suited for describing steadily evolving, path-following natural processes like motion capture data or speech. HMMs cannot represent incremental progress between behaviors, and sequences sampled from the models have unnatural segment durations, unsmooth transitions, and excessive rapid variation. We propose to address these problems by permitting the state variable to occupy positions between the discrete states, and present a concrete left-right model incorporating this idea. We call this intermediate-state HMMs. The state evolution remains Markovian. We describe training using the generalized EM-algorithm and present associated update formulas. An experiment shows that the intermediate-state model is capable of gradual transitions, with more natural durations and less noise in sampled sequences compared to a conventional HMM.
Place, publisher, year, edition, pages
2011. 1828-1831 p.
Markov models, HMMs, speech synthesis
IdentifiersURN: urn:nbn:se:kth:diva-44027ISI: 000316502200459ScopusID: 2-s2.0-84865717483ISBN: 978-1-61839-270-1OAI: oai:DiVA.org:kth-44027DiVA: diva2:449055
12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011; Florence; Italy; 27 August 2011 through 31 August 2011
FunderEU, FP7, Seventh Framework Programme, 256230
QC 201111082012-02-032011-10-192014-01-16Bibliographically approved