Federated Marginal Personalization for ASR Rescoring

We introduce federated marginal personalization (FMP), a novel method for continuously updating personalized neural network language models (NNLMs) on private devices using federated learning (FL). Instead of fine-tuning the parameters of NNLMs on personal data, FMP regularly estimates global and pe...

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Bibliographic Details
Published inarXiv.org
Main Authors Liu, Zhe, Peng, Fuchun
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.12.2020
Subjects
Customization
Error reduction
Neural networks
Privacy
Speech recognition
Words (language)
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Summary:We introduce federated marginal personalization (FMP), a novel method for continuously updating personalized neural network language models (NNLMs) on private devices using federated learning (FL). Instead of fine-tuning the parameters of NNLMs on personal data, FMP regularly estimates global and personalized marginal distributions of words, and adjusts the probabilities from NNLMs by an adaptation factor that is specific to each word. Our presented approach can overcome the limitations of federated fine-tuning and efficiently learn personalized NNLMs on devices. We study the application of FMP on second-pass ASR rescoring tasks. Experiments on two speech evaluation datasets show modest word error rate (WER) reductions. We also demonstrate that FMP could offer reasonable privacy with only a negligible cost in speech recognition accuracy.
ISSN:2331-8422