  Keyword spotting (KWS) is an important technique for speech applications,
which enables users to activate devices by speaking a keyword phrase. Although
a phoneme classifier can be used for KWS, exploiting a large amount of
transcribed data for automatic speech recognition (ASR), there is a mismatch
between the training criterion (phoneme recognition) and the target task (KWS).
Recently, multi-task learning has been applied to KWS to exploit both ASR and
KWS training data. In this approach, an output of an acoustic model is split
into two branches for the two tasks, one for phoneme transcription trained with
the ASR data and one for keyword classification trained with the KWS data. In
this paper, we introduce a cross attention decoder in the multi-task learning
framework. Unlike the conventional multi-task learning approach with the simple
split of the output layer, the cross attention decoder summarizes information
from a phonetic encoder by performing cross attention between the encoder
outputs and a trainable query sequence to predict a confidence score for the
KWS task. Experimental results on KWS tasks show that the proposed approach
achieves a 12% relative reduction in the false reject ratios compared to the
conventional multi-task learning with split branches and a bi-directional long
short-team memory decoder.
