In this paper, we propose a new method for noise robustness base on Gaussian Mixture
Model (GMM), and the method we proposed can estimate the noise feature effectively
and reduce noise effect by plain fashion, and we can retain the smoothing and continuity
from original feature in this way. Compared to the traditional feature transformation method
MMSE(Minimum Mean Square Error) which want to find a clean one, the different is that
the method we proposed only need to fine noise feature or the margin of noise effect and subtract
the noise to achieve more robustness effect than traditional methods. In the experiment
method, the test data pass through the trained noise classifier to judge the noise type and SNR,
and according to the result of classifier to choose the corresponding transformation model and
generate the noise feature by this model, and then we can use different weight linear combination
to generate noise feature, and finally apply simple subtraction to achieve noise reduction.
In the experiment, we use AURORA 2.0 corpus to estimate noise robustness performance,
and using traditional method can achieve 36:8% relative improvement than default, and the
our method can achieve 52:5% relative improvement, and compared to the traditional method
our method can attain 24:9% relative improvement.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0828112-195916 |
| Date | 28 August 2012 |
| Creators | Yeh, Bing-Feng |
| Contributors | Chung-Hsien Wu, Chia-Ping Chen, Wei-Bin Liang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0828112-195916 |
| Rights | unrestricted, Copyright information available at source archive |
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