Federated Marginal Personalization for ASR Rescoring

TL;DR

This paper proposes federated marginal personalization (FMP), a new method for updating personalized neural network language models on private devices, improving speech recognition accuracy while maintaining privacy.

Contribution

FMP introduces a novel approach to personalize NNLMs via marginal distribution estimation, avoiding fine-tuning on private data, and is effective for ASR rescoring.

Findings

FMP achieves modest WER reductions in ASR tasks.

FMP maintains privacy with negligible accuracy loss.

Efficiently learns personalized models on devices.

Abstract

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.