Low resource end-to-end spoken language understanding with capsule networks

Abstract

Designing a Spoken Language Understanding (SLU) system for command-and-control applications is challenging. Both Automatic Speech Recognition and Natural Language Understanding are language and application dependent to a great extent. Even with a lot of design effort, users often still have to know what to say to the system for it to do what they want. We propose to use an end-to-end SLU system that maps speech directly to semantics and that can be trained by the user through demonstrations. The user can teach the system a new command by uttering the command and subsequently demonstrating its meaning through an alternative interface. The system will learn the mapping from the spoken command to the task. The dependency on the user also allows different languages and non-standard or impaired speech as valid inputs. Teaching the system requires effort from the user, so it is crucial that the system learns quickly. In this paper we propose to use capsule networks for this task, which are believed to be data efficient. We discuss two architectures for using capsule networks. We analyse their performance and compare them with two baseline systems, one based on Non-negative Matrix Factorisation (NMF) which has been successful for this task and one encoder-decoder approach. We show that in most cases the capsule network performs better than the baseline systems. Furthermore, we demonstrate the versatility of the architecture by inferring speaker identity and the user’s word choice through multitask learning.