Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks
| Type of publication: | Conference paper |
| Citation: | VILLATORO-TELLO_ICASSP2023-2_2023 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing |
| Year: | 2023 |
| Month: | March |
| Abstract: | 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. |
| Keywords: | Cross-modal Attention, Human-Computer Interaction, speech recognition, Spoken Language Understanding, Word Consensus Networks |
| Projects |
Idiap UNIPHORE |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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