Detail výsledku
Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic Units
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.
keyword search, spoken term detection, acoustic unit discovery
@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 the Annual Conference of the International Speech Communication Association, INTERSPEECH",
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"
}Robustní zpracování nahrávek pro operativu a bezpečnost, MV, PROGRAM STRATEGICKÁ PODPORA ROZVOJE BEZPEČNOSTNÍHO VÝZKUMU ČR 2019-2025 (IMPAKT 1) PODPROGRAMU 1 SPOLEČNÉ VÝZKUMNÉ PROJEKTY (BV IMP1/1VS), VJ01010108, zahájení: 2020-10-01, ukončení: 2025-09-30, ukončen