Unified Neural Network Speech Recognition

The patent merges the acoustic model and language model into a unified neural network architecture where both models share parameters and are trained jointly. The acoustic model processes audio signals to extract features, while the language model processes text sequences, and both are integrated through shared embedding layers and joint optimization objectives, resolving the contradiction by combining previously separate models into a cohesive system that improves accuracy without proportionally increasing complexity.

The unified neural network is designed to perform multiple functions simultaneously: acoustic feature extraction, text encoding, recognition result generation, and model updating. The same network architecture handles both audio and text processing tasks, allowing the system to maintain lower complexity while achieving high accuracy through multi-functional integration rather than separate specialized models.

The patent implements dynamic adaptability by enabling the unified neural network to continuously update its parameters based on new recognition results. The system transitions from static pre-trained models to a dynamic system where the language model is updated using feedback from recognition outcomes, allowing the model to adapt to new speech patterns, accents, and contexts while maintaining computational efficiency through incremental learning rather than complete retraining.

The system incorporates feedback mechanisms where recognition results are fed back into the language model for continuous updating. The recognition results from the acoustic model and text processing are used to refine the language model's parameters, creating a closed-loop system that improves adaptability over time. This feedback-driven approach allows the model to learn from its own performance and adjust dynamically without requiring external retraining data.