期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2014
卷号：8
期号：8
页码：151-158
DOI：10.14257/ijseia.2014.8.8,14
出版社：SERSC
摘要：Speech recognition, the problem of performance degradation is the difference between the model training and recognition environments. Silence features normalized using the method as a way to reduce the inconsistency of such an environment. Silence features normalized way of existing in the low signal-to-noise ratio. Increase the energy level of the silence interval for speech and non-speech classification accuracy due to the falling. There is a problem in the recognition performance is degraded. This paper proposed a robust speech detection method in noisy environments using a SFN(silence feature normalization) and SEM(speech energy maximize). In the high signal-to-noise ratio for the proposed method was used to maximize the characteristics receive less characterized the effects of noise by the speech energy. Cepstral feature distribution of speech and non-speech characteristics in the low signal-to- noise ratio and improves the recognition performance. Result of the recognition experiment, recognition performance improved compared to the conventional method.
