Publication Details
Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic Units
keyword search, spoken term detection, acoustic unit discovery
End-to-end (E2E) keyword search (KWS) has emerged as an alternative and
complimentary approach to conventional key- word search which depends on the
output of automatic speech recognition (ASR) systems. While E2E methods greatly
sim- plify the KWS pipeline, they generally have worse performance than their
ASR-based counterparts, which can benefit from pretraining with untranscribed
data. In this work, we propose a method for pretraining E2E KWS systems with
untranscribed data, which involves using acoustic unit discovery (AUD) to obtain
discrete units for untranscribed data and then learning to locate sequences of
such units in the speech. We conduct exper- iments across languages and AUD
systems: we show that finetuning such a model significantly outperforms a model
trained from scratch, and the performance improvements are generally correlated
with the quality of the AUD system used for pretraining.
@inproceedings{BUT193671,
author="YUSUF, B. and ČERNOCKÝ, J. and SARAÇLAR, M.",
title="Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic Units",
booktitle="Proceedings of Interspeech 2024",
year="2024",
journal="Proceedings of Interspeech",
volume="2024",
number="9",
pages="5068--5072",
publisher="International Speech Communication Association",
address="Kos",
doi="10.21437/Interspeech.2024-1713",
issn="1990-9772",
url="https://www.isca-archive.org/interspeech_2024/yusuf24b_interspeech.pdf"
}