Publication Details
Probability-Aware Word-Confusion-Network-to-Text Alignment Approach for Intent Classification
Madikeri Srikanth
SHARMA, B.
KHALIL, D.
KUMAR, S.
NIGMATULINA, I.
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
GANAPATHIRAJU, A.
Word-Confusion-Networks, Cross-modal Alignment, Knowledge Distillation, Intent
Classification
Spoken Language Understanding (SLU) technologies have greatly improved due to the
effective pretraining of speech representations. A common requirement of
industry-based solutions is the portability to deploy SLU models in voice-
assistant devices. Thus, distilling knowledge from large text- based language
models has become an attractive solution for achieving good performance and
guaranteeing portability. In this paper, we introduce a novel architecture that
uses a cross- modal attention mechanism to extract bin-level contextual
embeddings from a word-confusion network (WNC) encod- ing such that these can be
directly compared and aligned with traditional text-based contextual embeddings.
This alignment is achieved using a recently proposed tokenwise constrastive loss
function. We validate our architecture's effectiveness by fine-tuning our
WCN-based pretrained model to do intent classification (IC) on the well-known
SLURP dataset. Ob- tained accuracy on the IC task (81%), depicts a 9.4% relative
improvement compared to a recent/equivalent E2E method
@inproceedings{BUT196786,
author="VILLATORO-TELLO, E. and MADIKERI, S. and SHARMA, B. and KHALIL, D. and KUMAR, S. and NIGMATULINA, I. and MOTLÍČEK, P. and GANAPATHIRAJU, A.",
title="Probability-Aware Word-Confusion-Network-to-Text Alignment Approach for Intent Classification",
booktitle="ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
year="2024",
pages="12617--12621",
publisher="IEEE Signal Processing Society",
address="Seoul",
doi="10.1109/ICASSP48485.2024.10445934",
isbn="979-8-3503-4485-1",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10445934"
}