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%.

利用音频一致性文本编码器，通过转换文本到音素再到嵌入，检测任意关键词并辅以混淆关键词生成，实现具有强辨别力的音频 - 文本嵌入验证器。在实验中，该方案在 Libriphrase 难数据集上表现优于现有技术，AUC 指标从 84.21% 提升至 92.7%，EER 指标从 23.36% 降低至 14.4%。