  We study a streamable attention-based encoder-decoder model in which either
the decoder, or both the encoder and decoder, operate on pre-defined,
fixed-size windows called chunks. A special end-of-chunk (EOC) symbol advances
from one chunk to the next chunk, effectively replacing the conventional
end-of-sequence symbol. This modification, while minor, situates our model as
equivalent to a transducer model that operates on chunks instead of frames,
where EOC corresponds to the blank symbol. We further explore the remaining
differences between a standard transducer and our model. Additionally, we
examine relevant aspects such as long-form speech generalization, beam size,
and length normalization. Through experiments on Librispeech and TED-LIUM-v2,
and by concatenating consecutive sequences for long-form trials, we find that
our streamable model maintains competitive performance compared to the
non-streamable variant and generalizes very well to long-form speech.

Audio and Speech Processing;Sound;Machine Learning