Abstract: The present invention provides an energy feature extraction method for noisy speech recognition. At first, noisy speech energy of an input noisy speech is computed. Next, the noise energy in the input noisy speech is estimated. Then, the estimated noise energy is subtracted from the noisy speech energy to obtain estimated clean speech energy. Finally, delta operations are performed on the log of the estimated clean speech energy to determine the energy derivative features for the noisy speech.

Abstract: A method of processing voice signals suitable for enhancing the speech discrimination ability of a hearing impaired person is disclosed. First, a voice signal is received, and the received voice signal is divided into a plurality of voice frames. A frequency spectrum analysis is conducted on one of the voice frames to estimate the effective bandwidth of the voice frame. Next, a frequency transposition process is performed on the voice signal so as to suit the auditory sensation bandwidth of a hearing impaired person. In addition, an energy compensation process is performed on the voice frame after performing the frequency transposition process so as to compensate the reduced energy brought by the frequency transposition process.

Abstract: The present invention provides a method and a system for determining reliable speech recognition coefficients in noisy environment, which can increase the recognition rate in a noisy environment, such as an in-car environment. The present invention utilizes the feature that most of the energy of in-car noise is concentrated in the low frequency band. Therefore, the input speech signal is filtered to remove the signal at the frequency range in which the noise energy is concentrated. Then, the energy contour of the speech signal is calculated, so as to determine the related speech recognition coefficients. Accordingly, the influence caused by the noise can be reduced, and the recognition rate for the noisy speech can be improved.

Abstract: A method for removing a background noise from a speech signal is provided, which comprises the following steps. First, an attenuation factor of a frequency band i is calculated. Then, a smoothing filtering is performed based on the attenuation factors of the frequency bands to calculate a forward attenuation factor and a backward attenuation factor of the frequency band i. Then, a linear combination is performed on the forward attenuation factor and the backward attenuation factor to calculate a smooth attenuation factor of the frequency band i. Afterwards, a speech spectrum estimation is calculated based on the smooth attenuation factor. Finally, a speech signal without the background noise is obtained by using an inverse Fourier transform.

Abstract: The present invention provides an energy feature extraction method for noisy speech recognition. At first, noisy speech energy of an input noisy speech is computed. Next, the noise energy in the input noisy speech is estimated. Then, the estimated noise energy is subtracted from the noisy speech energy to obtain estimated clean speech energy. Finally, delta operations are performed on the log of the estimated clean speech energy to determine the energy derivative features for the noisy speech.

Abstract: The present invention provides a method and a system for determining reliable speech recognition coefficients in noisy environment, which can increase the recognition rate in a noisy environment, such as an in-car environment. The present invention utilizes the feature that most of the energy of in-car noise is concentrated in the low frequency band. Therefore, the input speech signal is filtered to remove the signal at the frequency range in which the noise energy is concentrated. Then, the energy contour of the speech signal is calculated, so as to determine the related speech recognition coefficients. Accordingly, the influence caused by the noise can be reduced, and the recognition rate for the noisy speech can be improved.