  An end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR)
model was proposed recently to jointly perform speaker counting, speech
recognition and speaker identification. The model achieved a low
speaker-attributed word error rate (SA-WER) for monaural overlapped speech
comprising an unknown number of speakers. However, the E2E modeling approach is
susceptible to the mismatch between the training and testing conditions. It has
yet to be investigated whether the E2E SA-ASR model works well for recordings
that are much longer than samples seen during training. In this work, we first
apply a known decoding technique that was developed to perform single-speaker
ASR for long-form audio to our E2E SA-ASR task. Then, we propose a novel method
using a sequence-to-sequence model, called hypothesis stitcher. The model takes
multiple hypotheses obtained from short audio segments that are extracted from
the original long-form input, and it then outputs a fused single hypothesis. We
propose several architectural variations of the hypothesis stitcher model and
compare them with the conventional decoding methods. Experiments using
LibriSpeech and LibriCSS corpora show that the proposed method significantly
improves SA-WER especially for long-form multi-talker recordings.
