With the rise of low power speech-enabled devices, there is a growing demand
to quickly produce models for recognizing arbitrary sets of keywords. As with
many machine learning tasks, one of the most challenging parts in the model
creation process is obtaining a sufficient amount of training data. In this
paper, we explore the effectiveness of synthesized speech data in training
small, spoken term detection models of around 400k parameters. Instead of
training such models directly on the audio or low level features such as MFCCs,
we use a pre-trained speech embedding model trained to extract useful features
for keyword spotting models. Using this speech embedding, we show that a model
which detects 10 keywords when trained on only synthetic speech is equivalent
to a model trained on over 500 real examples. We also show that a model without
our speech embeddings would need to be trained on over 4000 real examples to
reach the same accuracy.

本文研究使用合成语音数据为小型的口语术语检测模型训练提取有用特征的预训练语音嵌入模型，相较于在 500 个真实示例上训练模型，只使用合成语音即可达到同等精度。