End-to-End Multi-Speaker Speech Recognition with Transformer
Table 4: Performance in terms of average WER [%] on the spatialized two-channel reverberant wsj1-2mix corpus after Nara-WPE.
['Model', 'dev', 'eval']
To alleviate this, we turned to an existing external dereverberation method to preprocess the input signals as a simple yet effective solution. The dereverberation is performed on the reverberated speech before it is added to the training dataset with anechoic data. Similarly, the reverberant test set is also preprocessed. In general, the WERs are dramatically decreased with the dereverberation method. For the RNN-based model, the WER on the evaluation set decreased by 41.1% relative, from 29.99% to 17.67%. Similar to the experiments under other conditions, the model with backend Transformer only is better than the RNN-based baseline model on the reverberant evaluation set by 13.8% relative WER. However, the Transformer-based frontend slightly degraded the performance. This may be due the window size of the attention being too small, as it only covers about 0.3 s of speech. Note that our systems are not trained through Nara-WPE, which is left for future work.