1. A method for real-time speaker recognition, comprising: obtaining speech data of a speaker to identify the speaker from a plurality of speakers; extracting, using a processor of a computer, a coarse feature of the speaker from the speech data; identifying the speaker as belonging to a pre-determined speaker cluster that is one of a plurality of partitions of the plurality of speakers and corresponds to a subset of a plurality of biometric signatures of the plurality of speakers, wherein identifying the speaker as belonging to the pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a speaker independent parameter representing the subset of the plurality of biometric signatures; further identifying, in response to identifying the speaker as belonging to the pre-determined speaker cluster, the speaker as belonging to a second level pre-determined speaker cluster that is one of a plurality of second level partitions of the pre-determined speaker cluster and corresponds to a second level subset of the subset of the plurality of biometric signatures, wherein identifying the speaker as belonging to the second level pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a second level speaker independent parameter representing the second level subset of the subset of the plurality of biometric signatures; extracting, using the processor of the computer, a plurality of Mel-Frequency Cepstral Coefficients (MFCC) and a plurality of Gaussian Mixture Model (GMM) components from the speech data; determining a biometric signature of the speaker based on the plurality of MFCC and the plurality of GMM components; and determining in real time, using the processor of the computer, an identity of the speaker by comparing the biometric signature of the speaker to the second level subset of the subset of the plurality of biometric signatures, wherein each of the plurality of biometric signatures is specific to one of the plurality of speakers.
1. A method for real-time speaker recognition, comprising: obtaining speech data of a speaker to identify the speaker from a plurality of speakers; extracting, using a processor of a computer, a coarse feature of the speaker from the speech data; identifying the speaker as belonging to a pre-determined speaker cluster that is one of a plurality of partitions of the plurality of speakers and corresponds to a subset of a plurality of biometric signatures of the plurality of speakers, wherein identifying the speaker as belonging to the pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a speaker independent parameter representing the subset of the plurality of biometric signatures; further identifying, in response to identifying the speaker as belonging to the pre-determined speaker cluster, the speaker as belonging to a second level pre-determined speaker cluster that is one of a plurality of second level partitions of the pre-determined speaker cluster and corresponds to a second level subset of the subset of the plurality of biometric signatures, wherein identifying the speaker as belonging to the second level pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a second level speaker independent parameter representing the second level subset of the subset of the plurality of biometric signatures; extracting, using the processor of the computer, a plurality of Mel-Frequency Cepstral Coefficients (MFCC) and a plurality of Gaussian Mixture Model (GMM) components from the speech data; determining a biometric signature of the speaker based on the plurality of MFCC and the plurality of GMM components; and determining in real time, using the processor of the computer, an identity of the speaker by comparing the biometric signature of the speaker to the second level subset of the subset of the plurality of biometric signatures, wherein each of the plurality of biometric signatures is specific to one of the plurality of speakers. 2. The method of claim 1 , wherein each of the plurality of partitions of the plurality of speakers comprises one of a plurality of speaker gender clusters, wherein each of the plurality of second level partitions of the pre-determined speaker cluster comprises one of a plurality of speaker age clusters, the method further comprising: further identifying, in response to identifying the speaker as belonging to the second level pre-determined speaker cluster, the speaker as belonging to a third level pre-determined speaker cluster that is one of a plurality of third level partitions of the second level pre-determined speaker cluster and corresponds to a third level subset of the second level subset of the subset of the plurality of biometric signatures, wherein identifying the speaker as belonging to the third level pre-determined speaker cluster is based on comparing the coarse feature of the speaker to a third level speaker independent parameter representing the third level subset of the second level subset of the subset of the plurality of biometric signatures, wherein each of the plurality of third level partitions of the second level pre-determined speaker cluster comprises one of a plurality of speaker native language clusters, and wherein comparing the biometric signature of the speaker to the second level subset of the subset of the plurality of biometric signatures is limited to comparing to the third level subset of the second level subset of the subset of the plurality of biometric signatures.