  Identifying keywords in an open-vocabulary context is crucial for
personalizing interactions with smart devices. Previous approaches to open
vocabulary keyword spotting dependon a shared embedding space created by audio
and text encoders. However, these approaches suffer from heterogeneous modality
representations (i.e., audio-text mismatch). To address this issue, our
proposed framework leverages knowledge acquired from a pre-trained
text-to-speech (TTS) system. This knowledge transfer allows for the
incorporation of awareness of audio projections into the text representations
derived from the text encoder. The performance of the proposed approach is
compared with various baseline methods across four different datasets. The
robustness of our proposed model is evaluated by assessing its performance
across different word lengths and in an Out-of-Vocabulary (OOV) scenario.
Additionally, the effectiveness of transfer learning from the TTS system is
investigated by analyzing its different intermediate representations. The
experimental results indicate that, in the challenging LibriPhrase Hard
dataset, the proposed approach outperformed the cross-modality correspondence
detector (CMCD) method by a significant improvement of 8.22% in area under the
curve (AUC) and 12.56% in equal error rate (EER).
