From audio to semantics: Approaches to end-to-end spoken language understanding
Abstract
Conventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to text, and a natural language understanding module that transforms the resulting text (or top N hypotheses) into a set of intents and arguments. These modules are typically optimized independently. In this paper, we formulate audio to semantic understanding as a sequence-to-sequence problem. We propose and compare various encoder-decoder based approaches that optimizes both modules jointly, in an end-to-end manner. We evaluate these methods on a real-world task. Our results show that having an intermediate text representation while jointly optimizing the full system improves accuracy of prediction.
Research Areas
