Hidden Markov Model with Binned Duration and Its Application

<p>Hidden Markov models (HMM) have been widely used in various applications such as speech processing and bioinformatics. However, the standard hidden Markov model requires state occupancy durations to be geometrically distributed, which can be inappropriate in some real-world applications where the distributions on state intervals deviate signi cantly from the geometric distribution, such as multi-modal distributions and heavy-tailed distributions. The hidden Markov model with duration (HMMD) avoids this limitation by explicitly incor- porating the appropriate state duration distribution, at the price of signi cant computational expense. As a result, the applications of HMMD are still quited limited. In this work, we present a new algorithm - Hidden Markov Model with Binned Duration (HMMBD), whose result shows no loss of accuracy compared to the HMMD decoding performance and a com- putational expense that only diers from the much simpler and faster HMM decoding by a constant factor. More precisely, we further improve the computational complexity of HMMD from (TNN +TND) to (TNN +TND ), where TNN stands for the computational com- plexity of the HMM, D is the max duration value allowed and can be very large and D generally could be a small constant value.</p>

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2010

Author dc:creator

• Jiang, Zuliang

Contributors dc:contributor

• Winters-Hilt, Stephen
• Summa, Christopher
• Zhu, Dongxiao

Repository record dc:identifier

https://scholarworks.uno.edu/td/1108

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-2089