Robust Speech Detection using SEM and SFN

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-tonoise ratio and improves the recognition performance. Result of the recognition experiment, recognition performance improved compared to the conventional method.