  The mechanism proposed here is for real-time speaker change detection in
conversations, which firstly trains a neural network text-independent speaker
classifier using in-domain speaker data. Through the network, features of
conversational speech from out-of-domain speakers are then converted into
likelihood vectors, i.e. similarity scores comparing to the in-domain speakers.
These transformed features demonstrate very distinctive patterns, which
facilitates differentiating speakers and enable speaker change detection with
some straight-forward distance metrics. The speaker classifier and the speaker
change detector are trained/tested using speech of the first 200 (in-domain)
and the remaining 126 (out-of-domain) male speakers in TIMIT respectively. For
the speaker classification, 100% accuracy at a 200 speaker size is achieved on
any testing file, given the speech duration is at least 0.97 seconds. For the
speaker change detection using speaker classification outputs, performance
based on 0.5, 1, and 2 seconds of inspection intervals were evaluated in terms
of error rate and F1 score, using synthesized data by concatenating speech from
various speakers. It captures close to 97% of the changes by comparing the
current second of speech with the previous second, which is very competitive
among literature using other methods.
