  Automatic speech recognition (ASR) models are frequently exposed to data
distribution shifts in many real-world scenarios, leading to erroneous
predictions. To tackle this issue, an existing test-time adaptation (TTA)
method has recently been proposed to adapt the pre-trained ASR model on
unlabeled test instances without source data. Despite decent performance gain,
this work relies solely on naive greedy decoding and performs adaptation across
timesteps at a frame level, which may not be optimal given the sequential
nature of the model output. Motivated by this, we propose a novel TTA
framework, dubbed SGEM, for general ASR models. To treat the sequential output,
SGEM first exploits beam search to explore candidate output logits and selects
the most plausible one. Then, it utilizes generalized entropy minimization and
negative sampling as unsupervised objectives to adapt the model. SGEM achieves
state-of-the-art performance for three mainstream ASR models under various
domain shifts.
