Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks
This work addresses the challenge of improving SLU systems for applications like voice assistants by benchmarking representations, but it is incremental as it builds on existing methods with new evaluations.
The paper tackled the intent classification problem in Spoken Language Understanding by evaluating text-based, lattice-based, and multimodal representations, finding that using word-consensus-networks improved performance by 5.5% relative to the 1-best setup, and crossmodal approaches achieved a 17.8% relative improvement, matching oracle performance.
In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup. We benchmark three types of systems to perform the SLU intent detection task: 1) text-based, 2) lattice-based, and a novel 3) multimodal approach. Our work provides a comprehensive analysis of what could be the achievable performance of different state-of-the-art SLU systems under different circumstances, e.g., automatically- vs. manually-generated transcripts. We evaluate the systems on the publicly available SLURP spoken language resource corpus. Our results indicate that using richer forms of Automatic Speech Recognition (ASR) outputs, namely word-consensus-networks, allows the SLU system to improve in comparison to the 1-best setup (5.5% relative improvement). However, crossmodal approaches, i.e., learning from acoustic and text embeddings, obtains performance similar to the oracle setup, a relative improvement of 17.8% over the 1-best configuration, being a recommended alternative to overcome the limitations of working with automatically generated transcripts.