Also considered are the effects of additive noise, using an additive power spectrum model and a constrained ARMA model (sum of an all‐pole model and a constant). A simple relationship among the cepstral coefficients, the predictor coefficients, and the reflection coefficients is established, and unlike the Itakura‐Saito distortions, it is shown that the L 2 distortion between two cepstra obtained from different‐order LPC models of the same speech data may contain an inherent nonzero bias. More specifically, the effects of LPC analysis order, additive noise, and pole movements upon some key characteristics of the LPC cepstrum are considered, such as the average and the norm of the cepstral vector. In this paper, some properties of the LPC cepstrum are investigated that are of important consideration when the recognizer is to be deployed in situations where mismatch between the training conditions and the testing conditions may potentially occur. Autoregressive model related cepstrum, or, briefly, LPC cepstrum, recently gained renewed attention because it was shown to be an effective representation in speech recognizer designs.
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