Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems

• URL: http://arxiv.org/abs/2306.02161v1
• Date: Sat, 3 Jun 2023 17:10:33 GMT
• Title: Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
• Authors: Manuele Rusci and Tinne Tuytelaars
• Abstract summary: 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.
• Score: 41.24728444810133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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