This paper reimagines some aspects of speech processing using speech encoders, specifically about extracting entities directly from speech, with no intermediate textual representation. In human-computer conversations, extracting entities such as names, postal addresses and email addresses from speech is a challenging task. In this paper, we study the impact of fine-tuning pre-trained speech encoders on extracting spoken entities in human-readable form directly from speech without the need for text transcription. We illustrate that such a direct approach optimizes the encoder to transcribe only the entity relevant portions of speech, ignoring the superfluous portions such as carrier phrases and spellings of entities. In the context of dialogs from an enterprise virtual agent, we demonstrate that the 1-step approach outperforms the typical 2-step cascade of first generating lexical transcriptions followed by text-based entity extraction for identifying spoken entities.

翻译：本文重新审视了语音处理中的若干方面，重点探讨如何利用语音编码器直接从语音中提取实体，而无需中间的文本表示。在人机对话中，从语音中提取姓名、邮政地址和电子邮件地址等实体是一项具有挑战性的任务。本文研究了微调预训练语音编码器对直接以人类可读形式从语音中提取口语实体的影响，且无需文本转录。我们证明，这种直接方法能够优化编码器，使其仅转录与实体相关的语音片段，忽略多余的语流部分（如引导短语及实体的拼读）。在企业虚拟智能体的对话场景中，我们验证了这种单步方法在识别口语实体方面优于传统的两步级联方法——即先生成词汇转录，再基于文本进行实体抽取。