First Order Hidden Markov Model - Theory and Implementation Issues
| Document type: | Researchreports |
|---|---|
| Full text: | |
| Author(s): | Mikael Nilsson |
| Title: | First Order Hidden Markov Model - Theory and Implementation Issues |
| Translated title: | Första Ordningens Gömda Markov Kedjor - Teori och Implementerings Aspekter |
| Series: | Research Report |
| Year: | 2005 |
| Issue: | 2 |
| Editor: | Mattias Dahl, Ingvar Claesson |
| ISSN: | 1103-1581 |
| Organization: | Blekinge Institute of Technology |
| Department: | School of Engineering - Dept. of Signal Processing (Sektionen för teknik – avd. för signalbehandling) School of Engineering S- 372 25 Ronneby +46 455 38 50 00 http://www.tek.bth.se/ |
| Authors e-mail: | Mikael.Nilsson@bth.se |
| Language: | English |
| Abstract: | This report explains the theory of Hidden Markov Models (HMMs). The emphasis is on the theory aspects in conjunction with the implementation issues that are encountered in a floating point processor. The main theory and implementation issues are based on the use of a Gaussian Mixture Model (GMM) as the state density in the HMM, and a Continuous Density Hidden Markov Model (CDHMM) is assumed. Suggestions and advice related to the implementation are given for a typical pattern recognition task. |
| Subject: | Signal Processing\Detection and Classification Mathematics\Probability and Statistics Signal Processing\General |
| Keywords: | HMM, GMM, SOFM, k-means, Baum-Welch, Viterbi, Pattern Recognition |
| URN: | urn:nbn:se:bth-00271 |
