  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
particular, 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.
