Improving Automatic Speech Recognition for Speakers Treated for Oral Cancer using Data Augmentation and LLM Error Correction

Abstract

In recent years, the performance of automatic speech recognition (ASR) systems has made considerable progress. Unfortunately, for people with speech impairments, such as people treated for oral cancer (OC), ASR performance is still lagging behind. The scarcity and variability of OC speech data makes development of ASR models for this type of speech difficult. In this work, we use data augmentation and large language model (LLM) error correction to mitigate this problem. We apply various augmentation techniques on a corpus of Dutch oral cancer speech to create synthetic data, and evaluate their effect on ASR performance. We finetune Whisper and Massively Multilingual Speech (MMS) models for each augmentation technique and observe, on average, an 8% relative decrease in Word Error Rate (WER) when including data created using text-to-speech (TTS). When employing LLMs for error correction, we see a further 21.4-26.2% relative decrease in WER for finetuned ASR models and a 10.0% relative decrease for non-finetuned models. Overall, we achieve a 40% relative WER decrease for Whisper and a 50% relative WER decrease for MMS, indicating that a combination of data augmentation and LLM correction is a viable strategy for the recognition of OC speech.