462 research outputs found
High-resolution embedding extractor for speaker diarisation
Speaker embedding extractors significantly influence the performance of
clustering-based speaker diarisation systems. Conventionally, only one
embedding is extracted from each speech segment. However, because of the
sliding window approach, a segment easily includes two or more speakers owing
to speaker change points. This study proposes a novel embedding extractor
architecture, referred to as a high-resolution embedding extractor (HEE), which
extracts multiple high-resolution embeddings from each speech segment. Hee
consists of a feature-map extractor and an enhancer, where the enhancer with
the self-attention mechanism is the key to success. The enhancer of HEE
replaces the aggregation process; instead of a global pooling layer, the
enhancer combines relative information to each frame via attention leveraging
the global context. Extracted dense frame-level embeddings can each represent a
speaker. Thus, multiple speakers can be represented by different frame-level
features in each segment. We also propose an artificially generating mixture
data training framework to train the proposed HEE. Through experiments on five
evaluation sets, including four public datasets, the proposed HEE demonstrates
at least 10% improvement on each evaluation set, except for one dataset, which
we analyse that rapid speaker changes less exist.Comment: 5pages, 2 figure, 3 tables, submitted to ICASS
Absolute decision corrupts absolutely: conservative online speaker diarisation
Our focus lies in developing an online speaker diarisation framework which
demonstrates robust performance across diverse domains. In online speaker
diarisation, outputs generated in real-time are irreversible, and a few
misjudgements in the early phase of an input session can lead to catastrophic
results. We hypothesise that cautiously increasing the number of estimated
speakers is of paramount importance among many other factors. Thus, our
proposed framework includes decreasing the number of speakers by one when the
system judges that an increase in the past was faulty. We also adopt dual
buffers, checkpoints and centroids, where checkpoints are combined with
silhouette coefficients to estimate the number of speakers and centroids
represent speakers. Again, we believe that more than one centroid can be
generated from one speaker. Thus we design a clustering-based label matching
technique to assign labels in real-time. The resulting system is lightweight
yet surprisingly effective. The system demonstrates state-of-the-art
performance on DIHARD 2 and 3 datasets, where it is also competitive in AMI and
VoxConverse test sets.Comment: 5pages, 2 figure, 4 tables, submitted to ICASS
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