Publication Details
Effectiveness of Text, Acoustic, and Lattice-Based Representations in Spoken Language Understanding Tasks
Madikeri Srikanth (FIT)
ZULUAGA-GOMEZ, J.
SHARMA, B.
Sarfjoo Seyyed Saeed
NIGMATULINA, I.
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
IVANOV, V.
GANAPATHIRAJU, A.
Speech Recognition, Human-computer Interaction, Spoken Language Understanding, Word Consensus Networks, Cross-modal Attention
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. manuallygenerated 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.
@inproceedings{BUT187787,
author="VILLATORO-TELLO, E. and MADIKERI, S. and ZULUAGA-GOMEZ, J. and SHARMA, B. and SARFJOO, S. and NIGMATULINA, I. and MOTLÍČEK, P. and IVANOV, V. and GANAPATHIRAJU, A.",
title="Effectiveness of Text, Acoustic, and Lattice-Based Representations in Spoken Language Understanding Tasks",
booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
year="2023",
pages="1--5",
publisher="IEEE Signal Processing Society",
address="Rhodes Island",
doi="10.1109/ICASSP49357.2023.10095168",
isbn="978-1-7281-6327-7",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095168"
}