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We propose a multi-task universal speech enhancement (MUSE) model that canperform five speech enhancement (SE) tasks: dereverberation, denoising, speechseparation (SS), target speaker extraction (TSE), and speaker counting. This isachieved by integrating two modules into an SE model: 1) an internal separationmodule that does both speaker counting and separation; and 2) a TSE module thatextracts the target speech from the internal separation outputs using targetspeaker cues. The model is trained to perform TSE if the target speaker cue isgiven and SS otherwise. By training the model to remove noise andreverberation, we allow the model to tackle the five tasks mentioned above witha single model, which has not been accomplished yet. Evaluation resultsdemonstrate that the proposed MUSE model can successfully handle multiple taskswith a single model.

A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction