标题： Whisper和GPT-2中情境偏差是否仍然有效？ 显示英文标题

标题： Can Contextual Biasing Remain Effective with Whisper and GPT-2?

摘要： 端到端自动语音识别（ASR）和大型语言模型，如Whisper和GPT-2，最近已被扩展以使用大量训练数据。 尽管有大量训练数据，特定任务中出现的不常见内容词可能仍然表现出较差的ASR性能，而上下文偏差可能是解决方法。 本文研究了将神经上下文偏差应用于Whisper并与GPT-2结合的效果。 具体而言，本文提出集成一个适应的树约束指针生成器（TCPGen）组件用于Whisper，并提出一种专用训练方案，在不修改任何Whisper模型参数的情况下动态调整最终输出。 在三个数据集上的实验显示，使用1000个词的偏差列表时，偏差词的错误显著减少。 当应用于领域特定数据时，上下文偏差更为有效，并且可以在不损失其通用性的情况下提升Whisper和GPT-2的性能。 显示英文摘要

摘要： End-to-end automatic speech recognition (ASR) and large language models, such as Whisper and GPT-2, have recently been scaled to use vast amounts of training data. Despite the large amount of training data, infrequent content words that occur in a particular task may still exhibit poor ASR performance, with contextual biasing a possible remedy. This paper investigates the effectiveness of neural contextual biasing for Whisper combined with GPT-2. Specifically, this paper proposes integrating an adapted tree-constrained pointer generator (TCPGen) component for Whisper and a dedicated training scheme to dynamically adjust the final output without modifying any Whisper model parameters. Experiments across three datasets show a considerable reduction in errors on biasing words with a biasing list of 1000 words. Contextual biasing was more effective when applied to domain-specific data and can boost the performance of Whisper and GPT-2 without losing their generality.