  Speaker identification in noisy audio recordings, specifically those from
collaborative learning environments, can be extremely challenging. There is a
need to identify individual students talking in small groups from other
students talking at the same time. To solve the problem, we assume the use of a
single microphone per student group without any access to previous large
datasets for training.
  This dissertation proposes a method of speaker identification using
cross-correlation patterns associated to an array of virtual microphones,
centered around the physical microphone. The virtual microphones are simulated
by using approximate speaker geometry observed from a video recording. The
patterns are constructed based on estimates of the room impulse responses for
each virtual microphone. The correlation patterns are then used to identify the
speakers. The proposed method is validated with classroom audios and shown to
substantially outperform diarization services provided by Google Cloud and
Amazon AWS.
