  In this paper, we present a novel approach to adapt a sequence-to-sequence
Transformer-Transducer ASR system to the keyword spotting (KWS) task. We
achieve this by replacing the keyword in the text transcription with a special
token <kw> and training the system to detect the <kw> token in an audio stream.
At inference time, we create a decision function inspired by conventional KWS
approaches, to make our approach more suitable for the KWS task. Furthermore,
we introduce a specific keyword spotting loss by adapting the
sequence-discriminative Minimum Bayes-Risk training technique. We find that our
approach significantly outperforms ASR based KWS systems. When compared with a
conventional keyword spotting system, our proposal has similar performance
while bringing the advantages and flexibility of sequence-to-sequence training.
Additionally, when combined with the conventional KWS system, our approach can
improve the performance at any operation point.
