Production federated keyword spotting via distillation, filtering, and joint federated-centralized training
We trained a keyword spotting model using federated learning on real user devices and observed significant improvements when the model was deployed for inference on phones. To compensate for data domains that are missing from on-device training caches, we employed joint federated-centralized training. And to learn in the absence of curated labels on-device, we formulated a confidence filtering strategy based on user-feedback signals for federated distillation. These techniques created models that significantly improved quality metrics in offline evaluations and user-experience metrics in live A/B experiments.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningKeyword SpottingSimilar Papers 제목 키워드 기반
Training Keyword Spotting Models on Non-IID Data with Federated Learning
We demonstrate that a production-quality keyword-spotting model can be trained on-device using federated learning and achieve comparable false accept and false reject rates to a centrally-trained model. To overcome the a…
Data AugmentationFederated LearningKeyword SpottingOn-Device Constrained Self-Supervised Speech Representation Learning for Keyword Spotting via Knowledge Distillation
Large self-supervised models are effective feature extractors, but their application is challenging under on-device budget constraints and biased dataset collection, especially in keyword spotting. To address this, we pr…
Keyword SpottingKnowledge DistillationRepresentation LearningSpeech Representation LearningMetric Learning for User-defined Keyword Spotting
The goal of this work is to detect new spoken terms defined by users. While most previous works address Keyword Spotting (KWS) as a closed-set classification problem, this limits their transferability to unseen terms. Th…
Keyword SpottingMetric LearningWeKws: A production first small-footprint end-to-end Keyword Spotting Toolkit
Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices. Recently, end-to-end (E2E) methods have become the most popular approach for on-device KWS t…
Keyword SpottingSemi-Supervised Federated Learning for Keyword Spotting
Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly expanded the ability to train machine le…
Federated LearningKeyword Spotting