Dual-Model Pinyin Correction for Accurate Text Transformation

The patent divides the pinyin-to-word transformation task into two separate models: a first model for pinyin correction and a second model for pinyin-to-word transformation. This segmentation allows each model to specialize in its specific function, improving overall accuracy while managing complexity through modular design. The correction model specifically addresses speech recognition errors from accents, noise, and homophones before the transformation model processes the corrected pinyin.

The patent applies preliminary correction action by using the first model to correct pinyin errors before the second model performs pinyin-to-word transformation. This preliminary correction addresses speech recognition errors from accents, noise, and homophones in advance, ensuring that the transformation model receives accurate input. The loss function design with different weights for correction and transformation tasks optimizes the balance between correction accuracy and processing efficiency.

The patent applies different loss functions to different parts of the training process: a first loss function for the correction model and a second loss function for the transformation model. This local quality approach allows each model to be optimized for its specific function with appropriate loss metrics. The correction model's loss function focuses on accuracy while the transformation model's loss function focuses on transformation quality, enabling precise control over each component's training without requiring a single complex unified loss function.