We propose a method for joint multichannel speech dereverberation with two spatial-aware multichannel tasks: direction-of-arrival (DOA) estimation and speech separation. The proposed method addresses  tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech processing tasks. The proposed method is inspired by the excellent sequence modeling capability of bi-directional encoder representation from transformers (BERT). Instead of utilizing explicit representations from pretraining in a self-supervised way , we utilizes transformer encoded hidden representations in a supervised way . Both multichannel spectral magnitude and spectral phase information  are encoded. Experimental result demonstrates the effectiveness of the proposed method.
We propose a method for joint multichannel speech dereverberation with two spatial-aware tasks: direction-of-arrival (DOA) estimation and speech separation. The proposed method addresses involved tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech enhancement tasks. The proposed method is inspired by the excellent sequence modeling capability of bidirectional encoder representation from transformers (BERT). Instead of utilizing explicit representations from pretraining in a self-supervised manner , we utilizes transformer encoded hidden representations in a supervised manner . Both multichannel spectral magnitude and spectral phase information of varying length utterances are encoded. Experimental result demonstrates the effectiveness of the proposed method.
D
multichannel
A
involved
R
processing
enhancement
R
bi-directional
bidirectional
R
way
manner
R
way
manner
A
of varying length utterances