  Modern Automatic Speech Recognition (ASR) systems can achieve high
performance in terms of recognition accuracy. However, a perfectly accurate
transcript still can be challenging to read due to grammatical errors,
disfluency, and other errata common in spoken communication. Many downstream
tasks and human readers rely on the output of the ASR system; therefore, errors
introduced by the speaker and ASR system alike will be propagated to the next
task in the pipeline. In this work, we propose a novel NLP task called ASR
post-processing for readability (APR) that aims to transform the noisy ASR
output into a readable text for humans and downstream tasks while maintaining
the semantic meaning of the speaker. In addition, we describe a method to
address the lack of task-specific data by synthesizing examples for the APR
task using the datasets collected for Grammatical Error Correction (GEC)
followed by text-to-speech (TTS) and ASR. Furthermore, we propose metrics
borrowed from similar tasks to evaluate performance on the APR task. We compare
fine-tuned models based on several open-sourced and adapted pre-trained models
with the traditional pipeline method. Our results suggest that finetuned models
improve the performance on the APR task significantly, hinting at the potential
benefits of using APR systems. We hope that the read, understand, and rewrite
approach of our work can serve as a basis that many NLP tasks and human readers
can benefit from.
