  Speaker attribution is required in many real-world applications, such as
meeting transcription, where speaker identity is assigned to each utterance
according to speaker voice profiles. In this paper, we propose to solve the
speaker attribution problem by using graph-based semi-supervised learning
methods. A graph of speech segments is built for each session, on which
segments from voice profiles are represented by labeled nodes while segments
from test utterances are unlabeled nodes. The weight of edges between nodes is
evaluated by the similarities between the pretrained speaker embeddings of
speech segments. Speaker attribution then becomes a semi-supervised learning
problem on graphs, on which two graph-based methods are applied: label
propagation (LP) and graph neural networks (GNNs). The proposed approaches are
able to utilize the structural information of the graph to improve speaker
attribution performance. Experimental results on real meeting data show that
the graph based approaches reduce speaker attribution error by up to 68%
compared to a baseline speaker identification approach that processes each
utterance independently.
