Private Language Model Adaptation for Speech Recognition. (arXiv:2110.10026v3 [eess.AS] UPDATED)

Web: http://arxiv.org/abs/2110.10026

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 …

arxiv language language model model speech speech recognition