Abstract: Preprocessing speech signals from an indirect conduction microphone. One exemplary method preprocesses the speech signal in two stages. In stage one, an external speech sample is characterized using an auto regression model, and coefficients from the model are convolved with the internal speech signal from the indirect conduction microphone to produce a pre-conditioned internal speech signal. In stage two, a training sound is received by the indirect conduction microphone and filtered through a low-pass filter. The result is then modeled using auto regression, and inverted to produce an inverted filter model. The pre-conditioned internal speech signal is convolved with the inverted filter model to remove negative or undesirable acoustic characteristics and loss from the speech signal from the indirect conduction microphone.

Abstract: Preprocessing speech signals from an indirect conduction microphone. One exemplary method preprocesses the speech signal in two stages. In stage one, an external speech sample is characterized using an auto regression model, and coefficients from the model are convolved with the internal speech signal from the indirect conduction microphone to produce a pre-conditioned internal speech signal. In stage two, a training sound is received by the indirect conduction microphone and filtered through a low-pass filter. The result is then modeled using auto regression, and inverted to produce an inverted filter model. The pre-conditioned internal speech signal is convolved with the inverted filter model to remove negative or undesirable acoustic characteristics and loss from the speech signal from the indirect conduction microphone.