As speech recognition model sizes and training data requirements grow, it is increasingly common for systems to only be available via APIs from online service providers rather than having direct access to models themselves. In this scenario it is challenging to adapt systems to a specific target domain. To address this problem we consider the recently released OpenAI Whisper ASR as an example of a large-scale ASR system to assess adaptation methods. An error correction based approach is adopted, as this does not require access to the model, but can be trained from either 1-best or N-best outputs that are normally available via the ASR API. LibriSpeech is used as the primary target domain for adaptation. The generalization ability of the system in two distinct dimensions are then evaluated. First, whether the form of correction model is portable to other speech recognition domains, and secondly whether it can be used for ASR models having a different architecture.

翻译：随着语音识别模型规模和训练数据需求的增长，系统越来越普遍地仅通过在线服务提供商的API访问，而非直接获取模型本身。在这种情况下，将系统适配到特定目标领域变得具有挑战性。为解决这一问题，我们以近期发布的OpenAI Whisper ASR作为大规模ASR系统的示例，评估适配方法。采用基于错误纠正的方法，该方法无需访问模型，但可通过通常从ASR API获取的1-best或N-best输出进行训练。将LibriSpeech作为主要适配目标领域，进一步评估系统在两个不同维度上的泛化能力：一是纠正模型的形式是否可移植到其他语音识别领域，二是其是否可用于具有不同架构的ASR模型。