Few-Shot Open-Set Learning for On-Device Customization of KeyWord
  Spotting Systems
Manuele Rusci Tinne Tuytelaars
A personalized KeyWord Spotting (KWS) pipeline typically requires the
training of a Deep Learning model on a large set of user-defined speech
utterances, preventing fast customization directly applied on-device. To fill
this gap, this paper investigates few-shot learning methods for open-set KWS
classification by combining a deep feature encoder with a prototype-based
classifier. With user-defined keywords from 10 classes of the Google Speech
Command dataset, our study reports an accuracy of up to 76% in a 10-shot
scenario while the false acceptance rate of unknown data is kept to 5%. In the
analyzed settings, the usage of the triplet loss to train an encoder with
normalized output features performs better than the prototypical networks
jointly trained with a generator of dummy unknown-class prototypes. This design
is also more effective than encoders trained on a classification problem and
features fewer parameters than other iso-accuracy approaches.