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While end-to-end ASR systems have proven competitive with the conventionalhybrid approach, they are prone to accuracy degradation when it comes to noisyand low-resource conditions. In this paper, we argue that, even in suchdifficult cases, some end-to-end approaches show performance close to thehybrid baseline. To demonstrate this, we use the CHiME-6 Challenge data as anexample of challenging environments and noisy conditions of everyday speech. Weexperimentally compare and analyze CTC-Attention versus RNN-Transducerapproaches along with RNN versus Transformer architectures. We also provide acomparison of acoustic features and speech enhancements. Besides, we evaluatethe effectiveness of neural network language models for hypothesis re-scoringin 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. worsethan the LF-MMI TDNN-F CHiME-6 Challenge baseline. With the Guided SourceSeparation based training data augmentation, this approach outperforms thehybrid baseline system by 2.7% WER abs. and the end-to-end system best knownbefore by 25.7% WER abs.

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