Pole-zero modeling of noisy speech and its application to vocoding

In this paper four pole-zero modeling algorithms that are based on high-order pole model fitting and decomposition method have been studied, and application to robust vocoding has been considered. They are autocorrelation prediction (AP), modified Yule-Walker (MYW), modified least square (MLS) and modified least square with autocorrelation compensation (MLSAC) methods. They involve only linear equations, and therefore are computationally efficient. Among those algorithms, the MLSAC method appears to be the most effective in spectral envelope estimation of noisy as well as clean speech. According to our simulation result, the improvement resulting from the use of the MLSAC pole-zero model for noisy speech is equivalent to increasing signal-to-noise ratio (SNR) by about 5 dB when SNR of input speech is 10 dB or less. The use of the MLSAC method in multi-rate vocoding is also discussed.