Abstract: A system for audio processing comprising an initial background statistical model system configured to generate an initial background statistical model using a predetermined sample size of audio data. A parameter computation system configured to generate parametric data for the audio data including cepstral and energy parameters. A background statistics computation system configured to generate preliminary background statistics for determining whether speech has been detected. A first speech detection system configured to determine whether speech was present in the initial sample of audio data. An adaptive background statistical model system configured to provide an adaptive background statistical model for use in continuous processing of audio data for speech detection. A parameter computation system configured to calculate cepstral parameters, energy parameters and other suitable parameters for speech detection.

Abstract: A system for audio processing comprising an initial background statistical model system configured to generate an initial background statistical model using a predetermined sample size of audio data. A parameter computation system configured to generate parametric data for the audio data including cepstral and energy parameters. A background statistics computation system configured to generate preliminary background statistics for determining whether speech has been detected. A first speech detection system configured to determine whether speech was present in the initial sample of audio data. An adaptive background statistical model system configured to provide an adaptive background statistical model for use in continuous processing of audio data for speech detection. A parameter computation system configured to calculate cepstral parameters, energy parameters and other suitable parameters for speech detection.

Abstract: According to a disclosed embodiment, an endpointer determines the background energy of a first portion of a speech signal, and a cepstral computing module extracts one or more features of the first portion. The endpointer calculates an average distance of the first portion based on the features. Subsequently, an energy computing module measures the energy of a second portion of the speech signal, and the cepstral computing module extracts one or more features of the second portion. Based on the features of the second portion, the endpointer calculates a distance of the second portion. Thereafter, the endpointer contrasts the energy of the second portion with the background energy of the first portion, and compares the distance of the second portion with the distance of the first portion. The second portion of the speech signal is classified by the endpointer as speech or non-speech based on the contrast and the comparison.

Abstract: According to a disclosed embodiment, an endpointer determines the background energy of a first portion of a speech signal, and a cepstral computing module extracts one or more features of the first portion. The endpointer calculates an average distance of the first portion based on the features. Subsequently, an energy computing module measures the energy of a second portion of the speech signal, and the cepstral computing module extracts one or more features of the second portion. Based on the features of the second portion, the endpointer calculates a distance of the second portion. Thereafter, the endpointer contrasts the energy of the second portion with the background energy of the first portion, and compares the distance of the second portion with the distance of the first portion. The second portion of the speech signal is classified by the endpointer as speech or non-speech based on the contrast and the comparison.

Abstract: According to a disclosed embodiment, an endpointer determines the background energy of a first portion of a speech signal, and a cepstral computing module extracts one or more features of the first portion. The endpointer calculates an average distance of the first portion based on the features. Subsequently, an energy computing module measures the energy of a second portion of the speech signal, and the cepstral computing module extracts one or more features of the second portion. Based on the features of the second portion, the endpointer calculates a distance of the second portion. Thereafter, the endpointer contrasts the energy of the second portion with the background energy of the first portion, and compares the distance of the second portion with the distance of the first portion. The second portion of the speech signal is classified by the endpointer as speech or non-speech based on the contrast and the comparison.

Abstract: According to a disclosed embodiment, an endpointer determines the background energy of a first portion of a speech signal, and a cepstral computing module extracts one or more features of the first portion. The endpointer calculates an average distance of the first portion based on the features. Subsequently, an energy computing module measures the energy of a second portion of the speech signal, and the cepstral computing module extracts one or more features of the second portion. Based on the features of the second portion, the endpointer calculates a distance of the second portion. Thereafter, the endpointer contrasts the energy of the second portion with the background energy of the first portion, and compares the distance of the second portion with the distance of the first portion. The second portion of the speech signal is classified by the endpointer as speech or non-speech based on the contrast and the comparison.

Abstract: A noise suppressor for suppressing noise in a source speech signal, where a method utilized by the noise suppressor comprises calculating a signal-to-noise ratio in the source speech signal, calculating a background noise estimate for a current frame of the source speech signal based on said current frame and at least one previous frame and in accordance with the signal-to-noise ratio, wherein the calculating the signal-to-noise ratio is carried out independent from the background noise estimate for the current frame, and subtracting the background noise estimate from the source speech signal to produce a noise-reduced speech signal. The method may also comprise calculating an over-subtraction parameter based on the signal-to-noise ratio, calculating a noise-floor parameter based on the signal-to-noise ratio, wherein the subtracting uses the over-subtraction parameter and the noise-floor parameter to produce the noise-reduced speech signal.