  One of the challenges in developing a high quality custom keyword spotting
(KWS) model is the lengthy and expensive process of collecting training data
covering a wide range of languages, phrases and speaking styles. We introduce
Synth4Kws - a framework to leverage Text to Speech (TTS) synthesized data for
custom KWS in different resource settings. With no real data, we found
increasing TTS phrase diversity and utterance sampling monotonically improves
model performance, as evaluated by EER and AUC metrics over 11k utterances of
the speech command dataset. In low resource settings, with 50k real utterances
as a baseline, we found using optimal amounts of TTS data can improve EER by
30.1% and AUC by 46.7%. Furthermore, we mix TTS data with varying amounts of
real data and interpolate the real data needed to achieve various quality
targets. Our experiments are based on English and single word utterances but
the findings generalize to i18n languages and other keyword types.
