Improving the training and application of Chinese language models for low-order grammar

This paper presents a method to enhance the training of low-order grammar language models in response to the issue of out-of-vocabulary (OOV) words and incorrect words in acoustic model and low-order grammar language model decoding, which results in a decrease in Chinese speech recognition accuracy. To address the problem of incorrect words caused by polyphonic Chinese character, multiple phonemes are added to the Chinese dictionary to improve the phoneme model training. Prior to training the low-order grammar language model, the one-dimensional word segmentation is converted into a vectorized segmentation structure for processing. This enables the training of language models to obtain different probability distributions for the same word, thereby reducing the occurrence of OOV words. Finally, this method is compared with traditional language model training methods and evaluated with the same acoustic parameters. The experiment demonstrates an 8.5% reduction in word error rate for speech recognition when using low-order grammar language models.