Magnitude spectral estimation via Poisson moments with application to speech recognition

We propose to use the Gamma filter as a continuous time spectral feature extractor for the preprocessing of speech signals. The Gamma filter is a simple analog structure which can be implemented as a cascade of identical first order lowpass filters. The filter generates the Poisson moments of the input signal at its taps. These moments carry spectral information about the recent history of the input signal and in return they can be used to construct a time-frequency representation alternative to the conventional methods of shortterm Fourier transform, cepstrum, etc. The appeal of the proposed method comes from the fact that in the analog domain the Poisson moments are readily available as a continuous time electrical signal and can be physically measured, rather than computed offline by a digital computer. With this convenience, the speed of the discrete time processor following the preprocessor is independent of the highest frequency of the input signal, but is constrained by the stationarity interval of the signal. The moments can be directly fed into artificial neural networks (ANNs) for tasks like classification and identification of timevarying signals like speech.