TRANSLATE REVERBERATED SPEECH TO ANECHOIC ONES: SPEECH DEREVERBERATION WITH BERT
Table 2: PESQ scores, number of parameters and inference times. Inference time is reported with 330 test utterances with a batch size of 10.
['[EMPTY]', '[ITALIC] T60', 'CNN2d', 'CL', 'CNN1d', 'LSTM', 'DEF']
We first compare the performance of WPE with BLSTM based methods and a basic BERT (DEF-BERT) based method. It is clear that DEF-BERT outperforms WPE consistently and achieves comparable performance with CL-BLSTM method with less parameters (16.9M vs 18.2M). The third experiment compares BLSTM-based sequence models with BERT-based sequence models using the same pre-sequence layers CNN2d, CL and CNN1d. BERT benefits from a stack of CNN2d and LSTM more than BLSTM, probably due to BERT favoring positional information.