Speaker diarisation addresses the question of 'who speaks when' in audio recordings, and has been studied extensively in the context of tasks such as broadcast news, meetings, etc. Performing diarisation on individual headset microphone (IHM) channels is sometimes assumed to easily give the desired output of speaker labelled segments with timing information. However, it is shown that given imperfect data, such as speaker channels with heavy crosstalk and overlapping speech, this is not the case. Deep neural networks (DNNs) can be trained on features derived from the concatenation of speaker channel features to detect which is the correct channel for each frame. Crosstalk features can be calculated and DNNs trained with or without overlapping speech to combat problematic data. A simple frame decision metric of counting occurrences is investigated as well as adding a bias against selecting nonspeech for a frame. Finally, two different scoring setups are applied to both datasets. The stricter SHEF setup finds diarisation error rates (DER) of 9.2% on TBL and 23.2% on RT07 while the NIST setup achieves 5.7% and 15.1% respectively.
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