A short-sparse representation of speech speaker recognition based on

The present invention discloses a voice short sparse representation based speaker recognition, speech signal processing belonging to the technical field of pattern recognition, which is intended to determine the existing methodologies in voice recognition rate limited condition data. Which mainly includes the following: ① From all speech samples for pretreatment, and then extract the MFCC and their first-order differential coefficient as the feature; Gaussian background model is trained by ② background speech database, and extracts the second feature vector as the super-Gaussian; ③ the training speech samples Gaussian supervector arranged together to form dictionary; ⑤ algorithm for solving sparse coefficient expressed, and the reconstructed signal, the recognition result is determined according to the minimum residuals. The present invention adaptively get super-Gaussian vectors, can greatly ease the voice data is insufficient to bring limited speaker characterized by personality problems; using the reconstructed residual sparse representation classification, capable of handling semantic information does not match the speaker leads model problem with the loss.