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Two methods for the analysis of the acoustic transmission of the respiratory system are presented. Continuous speech utterance is used as acoustic stimulation. The transmitted acoustic signal is recorded from various sites over the chest wall. The autoregressive (AR) method analyzes the power spectral density function of the transmitted sound, which depends heavily on the microphone assembly and the utterance. The method was applied to a screening problem and was tested on a small database that consisted of 19 normal and five abnormal patients. Using the first five AR coefficients and the prediction error of an AR(10) model as discriminating features the system screens all abnormals. An autoregressive moving average (ARMA) method that eliminates the dependence on microphone and utterances is proposed. In this method, the generalized least squares identification algorithm is used to estimate the ARMA transfer function of the respiratory system. The normal transfer function demonstrates a peak at the range of 130-250 Hz and sharp decrease in gain for higher frequencies. A pulmonary fibrotic patient demonstrated a peak at the same frequency range, a much higher gain in the high-frequency range, with an additional peak at about 700 Hz. It is concluded that speech-stimulated chest analysis systems have the potential of yielding important clinical information.