We propose a new method of generating meaningful embeddings for speech, changes to four commonly used meta learning approaches to enable them to perform keyword spotting in continuous signals and an approach of combining their outcomes into an end-to-end automatic speech recognition system to improve rare word recognition. We verify the functionality of each of our three contributions in two experiments exploring their performance for different amounts of classes (N-way) and examples per class (k-shot) in a few-shot setting. We find that the speech embeddings work well and the changes to the meta learning approaches also clearly enable them to perform continuous signal spotting. Despite the interface between keyword spotting and speech recognition being very simple, we are able to consistently improve word error rate by up to 5%.

中文翻译：

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元学习可改善端到端ASR中的稀有单词识别

我们提出了一种为语音生成有意义的嵌入的新方法，对四种常用的元学习方法进行了更改，以使它们能够在连续信号中执行关键字识别，并提出了一种将其结果结合到端到端自动语音识别系统中以进行改进的方法罕见的单词识别。我们在两个实验中验证了我们三项贡献中每一项的功能，这些实验探索了他们在几次镜头设置下针对不同数量的班级（N向）和每个班级的示例（K次）的表现。我们发现语音嵌入效果很好，对元学习方法的更改也清楚地使它们能够执行连续的信号点播。尽管关键字识别和语音识别之间的接口非常简单，但我们仍能够始终将字错误率提高多达5％。