论文标题
减少对语音培训数据的需求以建立口语理解系统
Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems
论文作者
论文摘要
缺乏语言学理解所需的标签（SLU）所需的标签，通常是构建可以直接处理语音输入的端到端（E2E）系统的主要障碍。相比之下，通常可以使用大量带有合适标签的文本数据。在本文中，我们提出了一种新颖的文本表示和培训方法，该方法允许使用这些文本资源有效地构建E2E SLU系统。有了非常有限的额外语音，我们表明这些模型可以进一步改进，以在靠近整个语音数据集建立的类似系统的级别上执行。使用三个不同的SLU数据集，在意图和实体任务上都证明了我们提出的方法的功效。通过纯文本培训，拟议的系统通过完整的语音培训可实现多达90％的性能。这些模型只有10％的语音数据，可显着提高到全部性能的97％。
The lack of speech data annotated with labels required for spoken language understanding (SLU) is often a major hurdle in building end-to-end (E2E) systems that can directly process speech inputs. In contrast, large amounts of text data with suitable labels are usually available. In this paper, we propose a novel text representation and training methodology that allows E2E SLU systems to be effectively constructed using these text resources. With very limited amounts of additional speech, we show that these models can be further improved to perform at levels close to similar systems built on the full speech datasets. The efficacy of our proposed approach is demonstrated on both intent and entity tasks using three different SLU datasets. With text-only training, the proposed system achieves up to 90% of the performance possible with full speech training. With just an additional 10% of speech data, these models significantly improve further to 97% of full performance.
