  Mapping two modalities, speech and text, into a shared representation space,
is a research topic of using text-only data to improve end-to-end automatic
speech recognition (ASR) performance in new domains. However, the length of
speech representation and text representation is inconsistent. Although the
previous method up-samples the text representation to align with acoustic
modality, it may not match the expected actual duration. In this paper, we
proposed novel representations match strategy through down-sampling acoustic
representation to align with text modality. By introducing a continuous
integrate-and-fire (CIF) module generating acoustic representations consistent
with token length, our ASR model can learn unified representations from both
modalities better, allowing for domain adaptation using text-only data of the
target domain. Experiment results of new domain data demonstrate the
effectiveness of the proposed method.
