ASR systems have become increasingly widespread in recent years. However,
their textual outputs often require post-processing tasks before they can be
practically utilized. To address this issue, we draw inspiration from the
multifaceted capabilities of LLMs and Whisper, and focus on integrating
multiple ASR text processing tasks related to speech recognition into the ASR
model. This integration not only shortens the multi-stage pipeline, but also
prevents the propagation of cascading errors, resulting in direct generation of
post-processed text. In this study, we focus on ASR-related processing tasks,
including Contextual ASR and multiple ASR post processing tasks. To achieve
this objective, we introduce the CPPF model, which offers a versatile and
highly effective alternative to ASR processing. CPPF seamlessly integrates
these tasks without any significant loss in recognition performance.

通过将多个与语音识别相关的 ASR 文本处理任务集成到 ASR 模型中，我们提出了 CPPF 模型，它不仅缩短了多阶段的处理流程，还避免了级联错误的传播，直接生成了后处理的文本。