  End-to-end neural diarization with encoder-decoder based attractors
(EEND-EDA) is a method to perform diarization in a single neural network. EDA
handles the diarization of a flexible number of speakers by using an LSTM-based
encoder-decoder that generates a set of speaker-wise attractors in an
autoregressive manner. In this paper, we propose to replace EDA with a
transformer-based attractor calculation (TA) module. TA is composed of a
Combiner block and a Transformer decoder. The main function of the combiner
block is to generate conversational dependent (CD) embeddings by incorporating
learned conversational information into a global set of embeddings. These CD
embeddings will then serve as the input for the transformer decoder. Results on
public datasets show that EEND-TA achieves 2.68% absolute DER improvement over
EEND-EDA. EEND-TA inference is 1.28 times faster than that of EEND-EDA.
