Selection of waveform units for corpus-based Mandarin speech synthesis based on decision trees and prosodic modification costs
Fu-Chiang Chou, Chiu-yu Tseng, Lin-shan Lee · 1999
The removal of noise from speech signals has applications ranging from speech enhancement for cellular communications to front ends for speech recognition systems. In this paper, we present a new nonlinear time-domain method called Noise-Regularized Adaptive Filtering. The approach is based on minimum mean-squared estimation using a modified cost function and allows designing both linear and nonlinear filters using only the observed noisy speech. 1. A GENERAL FRAMEWORK FOR MMSE ESTIMATION Given a noisy speech signal,