  The speech field is evolving to solve more challenging scenarios, such as
multi-channel recordings with multiple simultaneous talkers. Given the many
types of microphone setups out there, we present the UniX-Encoder. It's a
universal encoder designed for multiple tasks, and worked with any microphone
array, in both solo and multi-talker environments. Our research enhances
previous multi-channel speech processing efforts in four key areas: 1)
Adaptability: Contrasting traditional models constrained to certain microphone
array configurations, our encoder is universally compatible. 2) Multi-Task
Capability: Beyond the single-task focus of previous systems, UniX-Encoder acts
as a robust upstream model, adeptly extracting features for diverse tasks
including ASR and speaker recognition. 3) Self-Supervised Training: The encoder
is trained without requiring labeled multi-channel data. 4) End-to-End
Integration: In contrast to models that first beamform then process
single-channels, our encoder offers an end-to-end solution, bypassing explicit
beamforming or separation. To validate its effectiveness, we tested the
UniX-Encoder on a synthetic multi-channel dataset from the LibriSpeech corpus.
Across tasks like speech recognition and speaker diarization, our encoder
consistently outperformed combinations like the WavLM model with the BeamformIt
frontend.
