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利用语音编码器的某些方面，特别是直接从语音中提取实体，而无需中间文本表示。在人机对话中，从语音中提取姓名、邮寄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从语音中提取人类可读形式的语音实体的影响，无需文本转录。我们表明，这种直接方法优化了编码器，使其仅转录语音中与实体相关的部分，而忽略多余的部分，例如载体短语和实体的拼写方式。在企业虚拟助手的对话场景中，我们证明了这种一步法比典型的两步级联方法（首先生成词汇转录，然后进行基于文本的实体提取）在识别语音实体方面表现更优。