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：本文设想了使用语音编码器进行语音处理的某些方面,特别是直接从讲话中提取实体,而没有中间文本代表。在人-计算机对话中,从演讲中提取姓名、邮政地址和电子邮件地址等实体是一项艰巨的任务。在本文中,我们研究了经过预先培训的语音编码器对直接从讲话中提取可读口语实体的影响,而不需要文字抄录。我们说明,这种直接方法优化了编码器,只对实体的相关部分进行转录,而忽略了多余的部分,例如承运人的短语和实体的拼写。在企业虚拟代理的对话中,我们证明,一步骤方法优于典型的二步级制,先产生可读语言实体,然后以文字提取实体来识别口语实体。</s>