论文标题
联合的边际个性化ASR重新夺回
Federated Marginal Personalization for ASR Rescoring
论文作者
论文摘要
我们介绍了联合边际个性化（FMP），这是一种使用联合学习（FL）在私人设备上不断更新个性化神经网络语言模型（NNLMS）的新方法。 FMP没有在个人数据上微调NNLM的参数，而是定期估算单词的全局和个性化边缘分布，并通过针对每个单词的适应性因素调整NNLMS的概率。我们提出的方法可以克服联合微调的局限性，并有效地学习设备上的个性化NNLM。我们研究了FMP在第二次ASR撤退任务上的应用。两个语音评估数据集的实验显示了适度的单词错误率（WER）降低。我们还证明，FMP可以提供合理的隐私，而语音识别准确性的成本微不足道。
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.
