  Spotting user-defined/flexible keywords represented in text frequently uses
an expensive text encoder for joint analysis with an audio encoder in an
embedding space, which can suffer from heterogeneous modality representation
(i.e., large mismatch) and increased complexity. In this work, we propose a
novel architecture to efficiently detect arbitrary keywords based on an
audio-compliant text encoder which inherently has homogeneous representation
with audio embedding, and it is also much smaller than a compatible text
encoder. Our text encoder converts the text to phonemes using a
grapheme-to-phoneme (G2P) model, and then to an embedding using representative
phoneme vectors, extracted from the paired audio encoder on rich speech
datasets. We further augment our method with confusable keyword generation to
develop an audio-text embedding verifier with strong discriminative power.
Experimental results show that our scheme outperforms the state-of-the-art
results on Libriphrase hard dataset, increasing Area Under the ROC Curve (AUC)
metric from 84.21% to 92.7% and reducing Equal-Error-Rate (EER) metric from
23.36% to 14.4%.
