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State mixture modelling applied to speech and speaker recognition
Citation
Tran, D and Wagner, M and Zheng, T, State mixture modelling applied to speech and speaker recognition, Journal of Pattern Recognition Letter, 20, (11-13) pp. 1449-1456. ISSN 0167-8655 (1999) [Refereed Article]
DOI: doi:10.1016/S0167-8655(99)00113-0
Abstract
In state mixture modelling (SMM), the temporal structure of the observation sequences is represented by the state joint probability distribution where mixtures of states are considered. This technique is considered in an iterative scheme via maximum likelihood estimation. A fuzzy estimation approach is also introduced to cooperate with the SMM model. This new approach not only saves calculations from 2N TT (HMM direct calculation) and N 2T (Forward-backward algorithm) to just only 2NT calculations, but also achieves a better recognition result.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Research Division: | Information and Computing Sciences |
| Research Group: | Library and information studies |
| Research Field: | Human information interaction and retrieval |
| Objective Division: | Information and Communication Services |
| Objective Group: | Information systems, technologies and services |
| Objective Field: | Information systems, technologies and services not elsewhere classified |
| UTAS Author: | Zheng, T (Dr Tongtao Zheng) |
| ID Code: | 15899 |
| Year Published: | 1999 |
| Deposited By: | Asian Languages and Studies |
| Deposited On: | 1999-08-01 |
| Last Modified: | 2000-05-23 |
| Downloads: | 0 |
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