1. A processor implemented method for speech recognition of a speech signal comprising: within a processor: providing at least one codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, that are frequency weighted; and processing the speech signal for speech recognition comprising: extracting at least one feature vector from the speech signal and matching the feature vector with the entries of the codebook; providing at least one additional codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, without frequency weights; determining whether the speech signal corresponds to an utterance of a native speaker or to an utterance of a non-native speaker; and if it is determined that the speech signal corresponds to the utterance of a native speaker, using the at least one additional codebook comprising codebook entries without frequency weights for the speech recognition; or if it is determined that the speech signal corresponds to the utterance of a non-native speaker, using the at least one codebook comprising codebook entries that are frequency weighted for the speech recognition.
1. A processor implemented method for speech recognition of a speech signal comprising: within a processor: providing at least one codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, that are frequency weighted; and processing the speech signal for speech recognition comprising: extracting at least one feature vector from the speech signal and matching the feature vector with the entries of the codebook; providing at least one additional codebook comprising codebook entries, in particular, multivariate Gaussians of feature vectors, without frequency weights; determining whether the speech signal corresponds to an utterance of a native speaker or to an utterance of a non-native speaker; and if it is determined that the speech signal corresponds to the utterance of a native speaker, using the at least one additional codebook comprising codebook entries without frequency weights for the speech recognition; or if it is determined that the speech signal corresponds to the utterance of a non-native speaker, using the at least one codebook comprising codebook entries that are frequency weighted for the speech recognition. 3. The method according to claim 1 , wherein the entries of the at least one code-book are obtained by: detecting training speech signals corresponding to utterances of one or more native speakers; transforming the training speech signals into a frequency domain; applying a MEL filterbank to the transformed training speech signals in a log-Mel domain and saving the log value of each bank to derive first representations of the training speech signals; applying a Discrete Cosine Transform to the first representations of the training speech signals to obtain second transformed training speech signals; extracting feature vectors from the second transformed training speech signals; determining covariance matrices for the extracted feature vectors in the Discrete Cosine Transform domain; transforming the covariance matrices into the log-MEL domain; applying weights to the covariances of the covariance matrices in the log-MEL domain to obtain modified covariance matrices in the log-MEL domain; and transforming the modified covariance matrices from the log-MEL domain into the Linear Discriminant Analysis domain; determining Gaussians from the modified covariance matrices to obtain the entries of the at least one codebook.