标题： 面向设备的自适应知识蒸馏语音检测 显示英文标题

标题： Adaptive Knowledge Distillation for Device-Directed Speech Detection

摘要： 设备定向语音检测（DDSD）是一个二分类任务，用于将用户对语音助手（VA）的查询与背景语音或旁白对话区分开来。这对于实现自然的用户体验非常重要。为此，我们提出知识蒸馏（KD）来提高DDSD的准确性，同时确保高效的部署。具体而言，我们引入了一种新颖的自适应KD方法，从ASR大预训练声学编码器（教师）的一般表示中迁移知识。我们在（冻结）教师编码器上应用任务特定的适配器，并与学生模型一起在DDSD上进行联合训练。我们证明了所提出的自适应KD在关键词和无关键词（后续）调用中优于没有蒸馏的学生模型，分别在等错误率方面提高了+26%和+19%。我们还表明，这种方法适用于基于Transformer和Conformer的模型架构。 显示英文摘要

摘要： Device-directed speech detection (DDSD) is a binary classification task that separates the user's queries to a voice assistant (VA) from background speech or side conversations. This is important for achieving naturalistic user experience. To this end, we propose knowledge distillation (KD) to enhance DDSD accuracy while ensuring efficient deployment. Specifically, we introduce a novel adaptive KD method that transfers knowledge from general representations of an ASR large pre-trained acoustic encoder (teacher). We apply task-specific adapters, on top of the (frozen) teacher encoder, trained jointly with the student model on DDSD. We demonstrate that the proposed adaptive KD outperforms the student model without distillation in the keyword and keyword-free (follow-up) invocations, with an improvement of +26% and +19% in terms of Equal Error Rate, respectively. We also show that this approach generalizes across the transformer and conformer-based model architectures.