  The task of speaker change detection (SCD), which detects points where
speakers change in an input, is essential for several applications. Several
studies solved the SCD task using audio inputs only and have shown limited
performance. Recently, multimodal SCD (MMSCD) models, which utilise text
modality in addition to audio, have shown improved performance. In this study,
the proposed model are built upon two main proposals, a novel mechanism for
modality fusion and the adoption of a encoder-decoder architecture. Different
to previous MMSCD works that extract speaker embeddings from extremely short
audio segments, aligned to a single word, we use a speaker embedding extracted
from 1.5s. A transformer decoder layer further improves the performance of an
encoder-only MMSCD model. The proposed model achieves state-of-the-art results
among studies that report SCD performance and is also on par with recent work
that combines SCD with automatic speech recognition via human transcription.
