Improving Readability for Automatic Speech Recognition Transcription

by   Junwei Liao, et al.
University of Electronic Science and Technology of China

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.


Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model

Modern Automatic Speech Recognition (ASR) systems can achieve high perfo...

Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection

Although modern automatic speech recognition (ASR) systems can achieve h...

DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction

Conversational speech often consists of deviations from the speech plan,...

AfriNames: Most ASR models "butcher" African Names

Useful conversational agents must accurately capture named entities to m...

Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation

Fine-tuning pretrained language models (LMs) is a popular approach to au...

Leveraging Large Language Models for Exploiting ASR Uncertainty

While large language models excel in a variety of natural language proce...

Comparing Human and Machine Errors in Conversational Speech Transcription

Recent work in automatic recognition of conversational telephone speech ...

Please sign up or login with your details

Forgot password? Click here to reset