Bayesian Language Model Adaptation for Personalized Speech Recognition

In deployment environments for speech recognition models, diverse proper nouns such as personal names, song titles, and application names are frequently uttered. These proper nouns are often sparsely distributed within the training dataset, leading to performance degradation and limiting the practical utility of the models. Personalization strategies that leverage user-specific information, such as contact lists or search histories, have proven effective in mitigating performance degradation caused by rare words. In this study, we propose a novel personalization method for combining the scores of a general language model (LM) and a personal LM within a probabilistic framework. The proposed method entails low computational costs, storage requirements, and latency. Through experiments using a real-world dataset collected from the vehicle environment, we demonstrate that the proposed method effectively overcomes the out-of-vocabulary problem and improves recognition performance for rare words.