Estimation of LPC parameters of speech signals in noisy environment

The performance of LPC based algorithm deteriorates significantly in the presence of background noise. The present study proposes a new approach based on orthogonal least squares (OLS) algorithm with structure selection to obtain unbiased LPC parameters from noisy speech samples. Instead of fitting a fixed order model to all segments of speech, the algorithm selects the best possible model order for a given speech segment using an error reduction ratio (ERR) test. A noise model is appended to the conventional LPC model to make the LPC parameters unbiased. The proposed algorithm gives superior performance compared to the commonly used LPC based algorithm under high levels of noise.