Speech enhancement using dynamic synapse neural networks
Hassan H. Namarvar, Theodore W. Berger · The Journal of the Acoustical Society of America · 2003
An idea of speech enhancement using a Dynamic Synapse Neural Network (DSNN) with an extended Kalman filtering (EKF) training method is described. The goal of this study is to introduce a new methodology in better speech enhancement in the presence of continuous environment background noise, such as fans and air-conditioning units. The efficiency of this method is shown by applying it to noisy speech signals to remove recorded laboratory noise from signals at different signal-to-noise ratio levels. The preliminary results have been encouraging enough to justify our idea. To provide more noise robustness, this could be used as a pre-processing level in automatic speech recognition (ASR) systems. The proposed method would have a profound impact on the performance of ASR systems. [Work supported by DARPA CBS, NASA, and ONR.]