General purpose language models (LMs) encounter difficulties when processing domain-specific jargon and terminology, which are frequently utilized in specialized fields such as medicine or industrial settings. Moreover, they often find it challenging to interpret mixed speech that blends general language with specialized jargon. This poses a challenge for automatic speech recognition systems operating within these specific domains. In this work, we introduce a novel approach that integrates domain-specific or secondary LM into general-purpose LM. This strategy involves labeling, or "coloring", each word to indicate its association with either the general or the domain-specific LM. We develop an optimized algorithm that enhances the beam search algorithm to effectively handle inferences involving colored words. Our evaluations indicate that this approach is highly effective in integrating jargon into language tasks. Notably, our method substantially lowers the error rate for domain-specific words without compromising performance in the general domain.

翻译：通用语言模型在处理医学或工业等专业领域常用的特定术语和行话时存在困难。此外，它们往往难以理解混合了通用语言与专业术语的混合语音。这给在这些特定领域运行的自动语音识别系统带来了挑战。在本文中，我们提出了一种新颖的方法，将领域特定或辅助语言模型整合到通用语言模型中。该策略通过为每个词标注或“着色”，以指示其与通用语言模型或领域特定语言模型的关联。我们开发了一个优化算法，改进了束搜索算法，以有效处理涉及着色词的推理。评估表明，该方法在将术语融入语言任务中具有高效性。值得注意的是，我们的方法显著降低了领域特定词语的错误率，同时未损害通用领域的性能。