Result Details
Written Term Detection Improves Spoken Term Detection
SARAÇLAR, M.
End-to-end (E2E) approaches to keyword search
(KWS) are considerably simpler in terms of training and indexing
complexity when compared to approaches which use the output of
automatic speech recognition (ASR) systems. This simplification
however has drawbacks due to the loss of modularity. In partic-
ular, where ASR-based KWS systems can benefit from external
unpaired text via a language model, current formulations of E2E
KWS systems have no such mechanism. Therefore, in this paper,
we propose a multitask training objective which allows unpaired
text to be integrated into E2E KWS without complicating indexing
and search. In addition to training an E2E KWS model to retrieve
text queries from spoken documents, we jointly train it to retrieve
text queries from masked written documents. We show empirically
that this approach can effectively leverage unpaired text for KWS,
with significant improvements in search performance across a wide
variety of languages. We conduct analysis which indicates that
these improvements are achieved because the proposed method
improves document representations for words in the unpaired text.
Finally, we show that the proposed method can be used for domain
adaptation in settings where in-domain paired data is scarce or
nonexistent.
Keyword search, spoken term detection, keyword spotting, end-to-end keyword search, multitask learning, domain adaptation, masked language modeling.
@article{BUT193391,
author="YUSUF, B. and SARAÇLAR, M.",
title="Written Term Detection Improves Spoken Term Detection",
journal="IEEE-ACM Transactions on Audio Speech and Language Processing",
year="2024",
volume="32",
number="06",
pages="3213--3223",
doi="10.1109/TASLP.2024.3407476",
issn="2329-9290",
url="https://ieeexplore.ieee.org/document/10571348"
}