Improving Readability for Automatic Speech Recognition Transcription

Abstract

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.

References in corpus (13)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• A Deep Reinforced Model for Abstractive Summarization
• Unified Language Model Pre-training for Natural Language Understanding and Generation
• MASS: Masked Sequence to Sequence Pre-training for Language Generation
• The Microsoft 2017 Conversational Speech Recognition System
• Semi-supervised Sequence Learning
• Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study
• Modeling Multi-speaker Latent Space to Improve Neural TTS: Quick Enrolling New Speaker and Enhancing Premium Voice
• Controllable Abstractive Summarization
• Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting
• JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
• An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

Cited by in corpus (6)

• Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation
• FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
• Stacked DeBERT: All Attention in Incomplete Data for Text Classification
• Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
• Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens
• FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition