Abstract: A method of processing a speech signal comprises converting the speech signal to digital signals, converting the digital speech signal into short-time frames, applying a Fast Fourier Transform to each of the short-time frames to obtain an original spectrum, deriving a varied spectrum based on the original spectrum, applying discrete cosine transform to compute original cepstrum coefficients for the original spectrum and varied cepstrum coefficients for the varied spectrum and generating a set of frontend feature vectors for each of the short-time frames.

Abstract: A method of processing a speech signal comprises converting the speech signal to digital signals, converting the digital speech signal into short-time frames, applying a Fast Fourier Transform to each of the short-time frames to obtain an original spectrum, deriving a varied spectrum based on the original spectrum, applying discrete cosine transform to compute original cepstrum coefficients for the original spectrum and varied cepstrum coefficients for the varied spectrum and generating a set of frontend feature vectors for each of the short-time frames.

Abstract: A neural network is trained to transform distant-talking cepstrum coefficients, derived from a microphone array receiving speech from a speaker distant therefrom, into a form substantially similar to close-talking cepstrum coefficients that would be derived from a microphone close to the speaker, for providing robust hands-free speech and speaker recognition in adverse practical environments with existing speech and speaker recognition systems which have been trained on close-talking speech.

Abstract: A method and apparatus are disclosed for robust, text-independent (and text-dependent) speaker recognition in which identification of a speaker is based on selected spectral information from the speaker's voice. Traditionally, speaker recognition systems (i) render a speech sample in the frequency domain to produce a spectrum, (ii) produce cepstrum coefficients from the spectrum, (iii) produce a codebook from the cepstrum coefficients, and (iv) use the codebook as the feature measure for comparing training speech samples with testing speech samples. The present invention, on the other hand, introduces the important and previously unknown step of truncating the spectrum prior to producing the cepstrum coefficients. Through the use of selected spectra as the feature measure for speaker recognition, the present invention has been shown to yield significant improvements in performance over prior art systems.