Abstract: A method, computer program product, and computer system for receiving, by a computing device, an input signal. A first power spectral density estimate may be generated for a linear reverberant component associated with the input signal. A second power spectral density estimate may be generated for a non-linear reverberant component associated with the input signal. A power spectral density estimate may be generated by combining the first power spectral density estimate for the linear reverberant component and the second power spectral density estimate for the non-linear reverberant component. One or more parameters for at least one of the linear reverberant component and the non-linear reverberant component may be updated. One or more undesired signal components in an output signal resulting from the input signal may be reduced via residual echo suppression based upon, at least in part, updating the one or more parameters.

Abstract: A method for residual echo suppression is provided. Embodiments may include receiving an original reference signal and applying a distortion function to the original reference signal to generate a second signal. Embodiments may include generating a non-linear signal from the distortion function that does not include linear components of the original reference signal. Embodiments may also include calculating a residual echo power of a linear component and a non-linear component, wherein the linear component is based upon the original reference signal and the non-linear component is based upon the non-linear signal. Embodiments may further include applying a room model to each of the original reference signal and the non-linear signal and estimating a power associated with the original reference signal and the non-linear signal. Embodiments may include calculating a combined echo power estimate as a weighted sum of a weighted original reference signal power and a weighted non-linear signal power.

Abstract: A method, computer program product, and computer system for receiving, by a computing device, an input signal. A first power spectral density estimate may be generated for a linear reverberant component associated with the input signal. A second power spectral density estimate may be generated for a non-linear reverberant component associated with the input signal. A power spectral density estimate may be generated by combining the first power spectral density estimate for the linear reverberant component and the second power spectral density estimate for the non-linear reverberant component. One or more parameters for at least one of the linear reverberant component and the non-linear reverberant component may be updated. One or more undesired signal components in an output signal resulting from the input signal may be reduced via residual echo suppression based upon, at least in part, updating the one or more parameters.

Abstract: A method for residual echo suppression is provided. Embodiments may include receiving an original reference signal and applying a distortion function to the original reference signal to generate a second signal. Embodiments may include generating a non-linear signal from the distortion function that does not include linear components of the original reference signal. Embodiments may also include calculating a residual echo power of a linear component and a non-linear component, wherein the linear component is based upon the original reference signal and the non-linear component is based upon the non-linear signal. Embodiments may further include applying a room model to each of the original reference signal and the non-linear signal and estimating a power associated with the original reference signal and the non-linear signal. Embodiments may include calculating a combined echo power estimate as a weighted sum of a weighted original reference signal power and a weighted non-linear signal power.

Abstract: An arrangement is described for speech signal processing. An input microphone signal is received that includes a speech signal component and a noise component. The microphone signal is transformed into a frequency domain set of short-term spectra signals. Then speech formant components within the spectra signals are estimated based on detecting regions of high energy density in the spectra signals. One or more dynamically adjusted gain factors are applied to the spectra signals to enhance the speech formant components.

Abstract: A speech signal processing system is described for use with automatic speech recognition and hands free speech communication. A signal pre-processor module transforms an input microphone signal into corresponding speech component signals. A noise suppression module applies noise reduction to the speech component signals to generate noise reduced speech component signals. A speech reconstruction module produces corresponding synthesized speech component signals for distorted speech component signals. A signal combination block adaptively combines the noise reduced speech component signals and the synthesized speech component signals based on signal to noise conditions to generate enhanced speech component signals for automatic speech recognition and hands free speech communication.

Abstract: An arrangement is described for speech signal processing. An input microphone signal is received that includes a speech signal component and a noise component. The microphone signal is transformed into a frequency domain set of short-term spectra signals. Then speech formant components within the spectra signals are estimated based on detecting regions of high energy density in the spectra signals. One or more dynamically adjusted gain factors are applied to the spectra signals to enhance the speech formant components.

Abstract: A speech signal processing system is described for use with automatic speech recognition and hands free speech communication. A signal pre-processor module transforms an input microphone signal into corresponding speech component signals. A noise suppression module applies noise reduction to the speech component signals to generate noise reduced speech component signals. A speech reconstruction module produces corresponding synthesized speech component signals for distorted speech component signals. A signal combination block adaptively combines the noise reduced speech component signals and the synthesized speech component signals based on signal to noise conditions to generate enhanced speech component signals for automatic speech recognition and hands free speech communication.