Abstract
Despite remarkable achievements, automatic speech recognition (ASR) in low-resource scenarios still faces two challenges: high-quality data scarcity and high computational demands. This paper proposes EThai-ASR, the first to apply large language models (LLMs) to Thai ASR and create an efficient LLM-based ASR system. EThai-ASR comprises a speech encoder, a connection module and a Thai LLM decoder. To address the data scarcity and obtain a powerful speech encoder, EThai-ASR introduces a self-evolving data refinement strategy to refine weak labels, yielding an enhanced speech encoder. Moreover, we propose a pluggable sequence compression module used in the connection module with three modes designed to reduce the sequence length, thus decreasing computational demands while maintaining decent performance. Extensive experiments demonstrate that EThai-ASR has achieved state-of-the-art accuracy in multiple datasets. We release our refined text transcripts to promote further research.
Abstract (translated)
尽管自动语音识别（ASR）在资源有限的场景中已经取得了显著成就，但仍面临两大挑战：高质量数据稀缺和计算需求高。本文提出了EThai-ASR，这是首个将大型语言模型（LLMs）应用于泰语ASR并创建高效LLM基ASR系统的方案。EThai-ASR包含一个语音编码器、连接模块和一个泰语LLM解码器。为了应对数据稀缺问题，并增强语音编码器的能力，EThai-ASR引入了一种自我演进的数据精炼策略，用于改进弱标签，从而产生了一个性能更佳的语音编码器。此外，我们还提出了一种可插拔序列压缩模块，在连接模块中使用该模块并设计了三种模式以减少序列长度，从而在保持良好性能的同时降低计算需求。大量的实验表明，EThai-ASR已经在多个数据集中达到了最先进的准确率水平。我们将发布我们的精炼文本转录本，以促进进一步的研究。
URL
https://arxiv.org/abs/2505.22063
