End-to-end automatic speech recognition (ASR) systems often struggle to recognize rare name entities, such as personal names, organizations, or technical terms that are not frequently encountered in the training data. This paper presents Contextual Biasing Whisper (CB-Whisper), a novel ASR system based on OpenAI's Whisper model that performs keyword-spotting (KWS) before the decoder. The KWS module leverages text-to-speech (TTS) techniques and a convolutional neural network (CNN) classifier to match the features between the entities and the utterances. Experiments demonstrate that by incorporating predicted entities into a carefully designed spoken form prompt, the mixed-error-rate (MER) and entity recall of the Whisper model is significantly improved on three internal datasets and two open-sourced datasets that cover English-only, Chinese-only, and code-switching scenarios.

翻译：端到端自动语音识别（ASR）系统在识别训练数据中不常见的稀有命名实体（如人名、组织名称或技术术语）时通常面临困难。本文提出上下文偏置Whisper（CB-Whisper），一种基于OpenAI Whisper模型的新型ASR系统，该系统在解码器前执行关键词检测（KWS）。该KWS模块利用文本转语音（TTS）技术和卷积神经网络（CNN）分类器，实现实体与语音特征之间的匹配。实验表明，通过将预测实体整合至精心设计的口语化提示中，Whisper模型在覆盖纯英语、纯中文及语码转换场景的三个内部数据集和两个开源数据集上的混合错误率（MER）和实体召回率均得到显著提升。