Serialized Output Training by Learned Dominance
<|reference_start|>Serialized Output Training by Learned Dominance: 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.<|reference_end|>
@article{shi2024serialized,
    title={Serialized Output Training by Learned Dominance},
    author={Ying Shi, Lantian Li, Shi Yin, Dong Wang, Jiqing Han},
    journal={arXiv preprint arXiv:2407.03966},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03966},
    primaryClass={cs.SD cs.AI eess.AS}
}