Private Language Model Adaptation for Speech Recognition
This addresses the challenge of improving speech recognition accuracy on user devices while maintaining privacy, though it is incremental as it builds on existing federated learning methods.
The paper tackles the problem of adapting language models for speech recognition on private devices using federated learning, achieving relative word error rate reductions of 2.6% and 10.8% on two datasets.
Speech model adaptation is crucial to handle the discrepancy between server-side proxy training data and actual data received on local devices of users. With the use of federated learning (FL), we introduce an efficient approach on continuously adapting neural network language models (NNLMs) on private devices with applications on automatic speech recognition (ASR). To address the potential speech transcription errors in the on-device training corpus, we perform empirical studies on comparing various strategies of leveraging token-level confidence scores to improve the NNLM quality in the FL settings. Experiments show that compared with no model adaptation, the proposed method achieves relative 2.6% and 10.8% word error rate (WER) reductions on two speech evaluation datasets, respectively. We also provide analysis in evaluating privacy guarantees of our presented procedure.