روش ارتقاء سازه مبتنی بر LPC در کالمن فیلتر برای تقویت گفتار

In this work, we are concerned by a new iterative Kalman filtering scheme where a linear predictor model parameters are estimated from noisy speech. However, when only noise-corrupted speech is available, the enhancement performance of the Kalman filter is somewhat dependent on the accuracy of the linear prediction coefficients (LPCs) and excitation variance estimates. Nevertheless, linear prediction based speech (LPC) analysis is known to be sensitive to the presence of additive noise. To overcome this problem we present in this paper an analysis and application of the LPC-based formant enhancement method by modifying the log magnitude spectrum of the LPC model and then re-evaluating new LPCs to be applied on the Kalman filter. These enhanced LPCs are useful indicator of Kalman filter performance. Our enhancement experiments use a NOIZEUS speech corpus where the proposed method achieves higher objective and subjective results compared with other enhancement methods.