Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription

Andrei Andrusenko, Aleksandr Laptev, Ivan Medennikov


While end-to-end ASR systems have proven competitive with the conventional hybrid approach, they are prone to accuracy degradation when it comes to noisy and low-resource conditions. In this paper, we argue that, even in such difficult cases, some end-to-end approaches show performance close to the hybrid baseline. To demonstrate this, we use the CHiME-6 Challenge data as an example of challenging environments and noisy conditions of everyday speech. We experimentally compare and analyze CTC-Attention versus RNN-Transducer approaches along with RNN versus Transformer architectures. We also provide a comparison of acoustic features and speech enhancements. Besides, we evaluate the effectiveness of neural network language models for hypothesis re-scoring in low-resource conditions. Our best end-to-end model based on RNN-Transducer, together with improved beam search, reaches quality by only 3.8% WER abs. worse than the LF-MMI TDNN-F CHiME-6 Challenge baseline. With the Guided Source Separation based training data augmentation, this approach outperforms the hybrid baseline system by 2.7% WER abs. and the end-to-end system best known before by 25.7% WER abs.


 DOI: 10.21437/Interspeech.2020-1074

Cite as: Andrusenko, A., Laptev, A., Medennikov, I. (2020) Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription. Proc. Interspeech 2020, 319-323, DOI: 10.21437/Interspeech.2020-1074.


@inproceedings{Andrusenko2020,
  author={Andrei Andrusenko and Aleksandr Laptev and Ivan Medennikov},
  title={{Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={319--323},
  doi={10.21437/Interspeech.2020-1074},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1074}
}