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https://hdl.handle.net/10356/97488
Title: | An analysis of vector Taylor series model compensation for non-stationary noise in speech recognition | Authors: | Li, Haizhou Nguyen, Duc Hoang Ha Xiao, Xiong Chng, Eng Siong |
Keywords: | DRNTU::Engineering::Computer science and engineering | Issue Date: | 2012 | Source: | Nguyen, D. H. H., Xiao, X., Chng, E. S., & Li, H. (2012). An analysis of vector Taylor series model compensation for non-stationary noise in speech recognition. 2012 8th International Symposium on Chinese Spoken Language Processing (ISCSLP). | Conference: | International Symposium on Chinese Spoken Language Processing (8th : 2012 : Kowloon, Hong Kong) | Abstract: | In this paper, we investigate a feature conditioning method for the VTS-based model compensation. The VTS is a technique that predicts noisy acoustic model from clean acoustic model and noise model. It is noted that most of the previous studies use a single Gaussian noise model, which is unable to model noise statistics well, especially in non-stationary noisy environments. In this paper, we propose a combination of feature processing and VTS model compensation to handle non-stationary noise more efficiently. In the feature processing stage, the non-stationary characteristics of noise is reduced, hence the processed features is more suitable for VTS model compensation using single Gaussian noise model. Experimental analysis on the AURORA2 task shows that the proposed method has the potential to improve the performance of VTS method in non-stationary environments if good noise estimation is available. | URI: | https://hdl.handle.net/10356/97488 http://hdl.handle.net/10220/11868 |
DOI: | 10.1109/ISCSLP.2012.6423503 | Schools: | School of Computer Engineering | Research Centres: | Temasek Laboratories | Rights: | © 2012 IEEE. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SCSE Conference Papers |
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