  The use of Transformer architectures has facilitated remarkable progress in
speech enhancement. Training Transformers using substantially long speech
utterances is often infeasible as self-attention suffers from quadratic
complexity. It is a critical and unexplored challenge for a Transformer-based
speech enhancement model to learn from short speech utterances and generalize
to longer ones. In this paper, we conduct comprehensive experiments to explore
the length generalization problem in speech enhancement with Transformer. Our
findings first establish that position embedding provides an effective
instrument to alleviate the impact of utterance length on Transformer-based
speech enhancement. Specifically, we explore four different position embedding
schemes to enable length generalization. The results confirm the superiority of
relative position embeddings (RPEs) over absolute PE (APEs) in length
generalization.
