  Utterance clustering is one of the actively researched topics in audio signal
processing and machine learning. This study aims to improve the performance of
utterance clustering by processing multichannel (stereo) audio signals.
Processed audio signals were generated by combining left- and right-channel
audio signals in a few different ways and then extracted embedded features
(also called d-vectors) from those processed audio signals. This study applied
the Gaussian mixture model for supervised utterance clustering. In the training
phase, a parameter sharing Gaussian mixture model was conducted to train the
model for each speaker. In the testing phase, the speaker with the maximum
likelihood was selected as the detected speaker. Results of experiments with
real audio recordings of multi-person discussion sessions showed that the
proposed method that used multichannel audio signals achieved significantly
better performance than a conventional method with mono audio signals in more
complicated conditions.
