A Purely End-to-end System for Multi-speaker Speech Recognition
Table 6: Comparison with conventional approaches
['Method', 'WER (%)']
It was evaluated under the same evaluation data and metric as in Isik et al. However, there are differences in the size of training data and the options in decoding step. Therefore, it is not a fully matched condition. The word error rate (WER) reported in Isik et al. The proposed end-to-end ASR gives an 8.4% relative reduction in WER even though our model does not require any explicit frame-level labels such as phonetic alignment, or clean signal reference, and does not use a phonetic lexicon for training. Although this is an unfair comparison, our purely end-to-end system outperformed a hybrid system for multi-speaker speech recognition.