Effectiveness of Text, Acoustic, and Lattice-Based Representations in Spoken Language Understanding Tasks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU150722" target="_blank" >RIV/00216305:26230/23:PU150722 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095168" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095168</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP49357.2023.10095168" target="_blank" >10.1109/ICASSP49357.2023.10095168</a>
Alternative languages
Result language
angličtina
Original language name
Effectiveness of Text, Acoustic, and Lattice-Based Representations in Spoken Language Understanding Tasks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISBN
978-1-7281-6327-7
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
IEEE Signal Processing Society
Place of publication
Rhodes Island
Event location
Rhodes Island, Greece
Event date
Jun 4, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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