1. A method for speaker diarization, comprising the steps of: obtaining pre-trained acoustic models of individual speakers and/or groups of speakers; receiving speech data with multiple speakers; dividing the speech data into frames; and for each of a plurality of frames, determining an acoustic feature vector modified to include elements representing for one or more of the pre-trained acoustic models, a probability that the speaker of the frame is the speaker of the pre-trained acoustic model; and segmenting or clustering the received speech using the modified feature vectors of the plurality of frames, wherein said steps are implemented in either of: a) computer hardware configured to perform said steps, or b) computer software embodied in a non-transitory, tangible, computer-readable storage medium.
1. A method for speaker diarization, comprising the steps of: obtaining pre-trained acoustic models of individual speakers and/or groups of speakers; receiving speech data with multiple speakers; dividing the speech data into frames; and for each of a plurality of frames, determining an acoustic feature vector modified to include elements representing for one or more of the pre-trained acoustic models, a probability that the speaker of the frame is the speaker of the pre-trained acoustic model; and segmenting or clustering the received speech using the modified feature vectors of the plurality of frames, wherein said steps are implemented in either of: a) computer hardware configured to perform said steps, or b) computer software embodied in a non-transitory, tangible, computer-readable storage medium. 9. The method of claim 1 , wherein the modified acoustic feature vector comprises an extended vector including added elements representing for respective pre-trained acoustic models, a probability that the speaker of the frame is the speaker of the respective pre-trained acoustic model.