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 [BibTeX] [Marc21]
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
Madikeri, Srikanth
Juan, Zuluaga-Gomez.
Sharma, Bidisha
Sarfjoo, Seyyed Saeed
Iuliia, Nigmatulina
Motlicek, Petr
Ivanov, Alexei V.
Ganapathiraju, Aravind
Added by: [UNK]
Total mark: 0
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