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领域特定术语和行话时面临困难，这些术语常用于医学或工业等专门领域。此外，它们往往难以解释混合了通用语言与专门行话的混合语音。这对在这些特定领域运行的自动语音识别系统构成了挑战。在本工作中，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。该策略涉及对每个词进行标记或"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。我们开发了一种优化算法，改进了集束搜索算法以有效处理涉及着色词的推理。我们的评估表明，该方法在将行话整合到语言任务中非常有效。值得注意的是，我们的方法显著降低了领域特定词的错误率，同时不影响通用领域的性能。