Thursday 13 March 2025
The quest for efficient and accurate automatic speech recognition (ASR) has been ongoing for decades. One of the primary challenges in this field is reducing the computational cost while maintaining the accuracy of the system. To tackle this issue, researchers have turned to large language models (LLMs), which excel at rewriting tasks such as text style transfer and grammatical error correction.
A recent paper proposes an innovative approach to ASR post-editing using LLMs. The authors introduce a new representation for compact rewrites, inspired by phrase-based statistical machine translation. This method allows for more efficient decoding while maintaining the accuracy of the system.
The key insight behind this approach is that many rewriting tasks involve repeating spans of text in the input. By exploiting these common sub-strings between the input and output, researchers can create more compact representations of the rewrite process. These representations can then be used to reduce the computational cost at inference time.
The authors compare their new representation with a previously proposed span-based representation, which uses a numerical indexing into the input sequence followed by a target phrase. Their results show that the new phrase-based representation outperforms the span-based approach in terms of efficiency-accuracy trade-off.
To put this into perspective, consider an ASR system that is tasked with correcting errors in transcribed speech. The system can be fine-tuned on examples that map inputs to their compact rewrite representations. At inference time, these representations are used to generate complete rewrites. By using the phrase-based representation, the system can reduce its output length by 50-60% compared to the span-based approach while maintaining a similar level of accuracy.
The implications of this research are significant. For one, it enables the development of more efficient ASR systems that can be deployed on a large scale. This has far-reaching potential for applications such as voice assistants and speech-to-text software.
Additionally, the authors’ work highlights the importance of exploring new representations for rewriting tasks. By leveraging the overlap between input and output text, researchers can create more compact and efficient representations that improve the overall performance of ASR systems.
The paper also touches on the idea of speculative decoding, a technique used to accelerate large language model decoding. This involves generating multiple possible outputs simultaneously, allowing the system to take advantage of parallel processing capabilities. The authors demonstrate how this approach can be combined with their phrase-based representation to further reduce computational costs.
Overall, this research presents an exciting development in the field of ASR post-editing.
Cite this article: “Efficient Automatic Speech Recognition Post-Editing with Large Language Models”, The Science Archive, 2025.
Automatic Speech Recognition, Large Language Models, Post-Editing, Efficient Decoding, Compact Rewrites, Phrase-Based Representation, Span-Based Approach, Speculative Decoding, Parallel Processing, Asr Systems
