Hidden Markov Model Diarisation With Speaker Location Information

Speaker diarisation methods often rely on speaker embeddings to cluster together the segments of audio that are uttered by the same speaker. When the audio is captured using a microphone array, it is possible to estimate the locations of where the sounds originate from. This location information may be complementary to the speaker embeddings in the diarisation processes. This report proposes to extend the Hidden Markov Model (HMM) clustering method, to enable the use of speaker location information. The HMM observation log-likelihood for the speaker location can take the form of a KL-divergence, when the speaker location is represented as a discrete posterior distribution of the probabilities that the sound originated from each possible location. Experimental results on a Microsoft rich meeting transcription task show that using speaker location information with the proposed HMM modification can yield performance improvements over using speaker embeddings alone.