Personalized Speech Recognition via User Image Analysis

The patent segments the generic neural network model into multiple user-specific models by introducing a user identification module that divides users into different groups (e.g., children, adults, seniors) and selects appropriate models for each group. This allows the system to maintain multiple specialized models without requiring complex individual training for each user.

The patent applies preliminary action by pre-training multiple neural network models for different user characteristics before actual use. The system prepares various speech recognition models, natural language understanding models, and text-to-speech models in advance for different user groups, so that when a user interacts with the system, the appropriate pre-trained model is already available for immediate use.

The patent introduces an intermediary module (user identification module and model selection module) that mediates between the user input and the multiple neural network models. This intermediary layer manages the complexity by automatically identifying user characteristics and selecting the appropriate pre-trained models, shielding the user from the underlying system complexity.

The patent creates universal model families that can serve multiple user groups. Instead of creating entirely separate systems for each user, the system uses universal architectures (speech recognition, natural language understanding, text-to-speech) that are trained with different parameters for different user groups, reducing overall system complexity while maintaining adaptability.