ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction

• URL: http://arxiv.org/abs/2412.03075v1
• Date: Wed, 04 Dec 2024 06:52:10 GMT
• Title: ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction
• Authors: Victor Junqiu Wei, Weicheng Wang, Di Jiang, Yuanfeng Song, Lu Wang,
• Abstract summary: This paper studies ASR error correction in the Chinese language. To the best of our knowledge, it is the first Chinese ASR error correction benchmark. Inspired by the recent advances in emphlarge language models (LLMs), we investigate how to harness the power of LLMs to correct ASR errors.
• Score: 20.04650481108717
• License:
• Abstract: Automatic speech Recognition (ASR) is a fundamental and important task in the field of speech and natural language processing. It is an inherent building block in many applications such as voice assistant, speech translation, etc. Despite the advancement of ASR technologies in recent years, it is still inevitable for modern ASR systems to have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc. Therefore, the error correction in ASR is crucial. Motivated by this, this paper studies ASR error correction in the Chinese language, which is one of the most popular languages and enjoys a large number of users in the world. We first create a benchmark dataset named \emph{ASR-EC} that contains a wide spectrum of ASR errors generated by industry-grade ASR systems. To the best of our knowledge, it is the first Chinese ASR error correction benchmark. Then, inspired by the recent advances in \emph{large language models (LLMs)}, we investigate how to harness the power of LLMs to correct ASR errors. We apply LLMs to ASR error correction in three paradigms. The first paradigm is prompting, which is further categorized as zero-shot, few-shot, and multi-step. The second paradigm is finetuning, which finetunes LLMs with ASR error correction data. The third paradigm is multi-modal augmentation, which collectively utilizes the audio and ASR transcripts for error correction. Extensive experiments reveal that prompting is not effective for ASR error correction. Finetuning is effective only for a portion of LLMs. Multi-modal augmentation is the most effective method for error correction and achieves state-of-the-art performance.

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