  Serialized Output Training (SOT) has showcased state-of-the-art performance
in multi-talker speech recognition by sequentially decoding the speech of
individual speakers. To address the challenging label-permutation issue, prior
methods have relied on either the Permutation Invariant Training (PIT) or the
time-based First-In-First-Out (FIFO) rule. This study presents a model-based
serialization strategy that incorporates an auxiliary module into the Attention
Encoder-Decoder architecture, autonomously identifying the crucial factors to
order the output sequence of the speech components in multi-talker speech.
Experiments conducted on the LibriSpeech and LibriMix databases reveal that our
approach significantly outperforms the PIT and FIFO baselines in both 2-mix and
3-mix scenarios. Further analysis shows that the serialization module
identifies dominant speech components in a mixture by factors including
loudness and gender, and orders speech components based on the dominance score.
