Sequence to multi-sequence learning via conditional chain mapping for mixture signals

Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe*, Yusuke Fujita, Jiaming Xu, Bo Xu, Lei Xie


研究成果: Conference article査読

14 被引用数 (Scopus)


Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on one-to-many sequence transduction problems, such as extracting multiple sequential sources from a mixture sequence. We extend the standard sequence-to-sequence model to a conditional multi-sequence model, which explicitly models the relevance between multiple output sequences with the probabilistic chain rule. Based on this extension, our model can conditionally infer output sequences one-by-one by making use of both input and previously-estimated contextual output sequences. This model additionally has a simple and efficient stop criterion for the end of the transduction, making it able to infer the variable number of output sequences. We take speech data as a primary test field to evaluate our methods since the observed speech data is often composed of multiple sources due to the nature of the superposition principle of sound waves. Experiments on several different tasks including speech separation and multi-speaker speech recognition show that our conditional multi-sequence models lead to consistent improvements over the conventional non-conditional models.

ジャーナルAdvances in Neural Information Processing Systems
出版ステータスPublished - 2020
イベント34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online
継続期間: 2020 12月 62020 12月 12

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 信号処理


「Sequence to multi-sequence learning via conditional chain mapping for mixture signals」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。