Few-Shot Keyword Spotting in Any Language

by   Mark Mazumder, et al.

We introduce a few-shot transfer learning method for keyword spotting in any language. Leveraging open speech corpora in nine languages, we automate the extraction of a large multilingual keyword bank and use it to train an embedding model. With just five training examples, we fine-tune the embedding model for keyword spotting and achieve an average F1 score of 0.75 on keyword classification for 180 new keywords unseen by the embedding model in these nine languages. This embedding model also generalizes to new languages. We achieve an average F1 score of 0.65 on 5-shot models for 260 keywords sampled across 13 new languages unseen by the embedding model. We investigate streaming accuracy for our 5-shot models in two contexts: keyword spotting and keyword search. Across 440 keywords in 22 languages, we achieve an average streaming keyword spotting accuracy of 85.2 promising initial results on keyword search.


Teaching keyword spotters to spot new keywords with limited examples

Learning to recognize new keywords with just a few examples is essential...

Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data

Continuous Speech Keyword Spotting (CSKS) is the problem of spotting key...

LipLearner: Customizable Silent Speech Interactions on Mobile Devices

Silent speech interface is a promising technology that enables private c...

TempAdaCos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping

Few-shot keyword spotting (KWS) systems often utilize a sliding window o...

Phraseformer: Multimodal Key-phrase Extraction using Transformer and Graph Embedding

Background: Keyword extraction is a popular research topic in the field ...

Please sign up or login with your details

Forgot password? Click here to reset