Method and system for personalizing machine learning models
Patent Information
- Application Number
- DE602022021845
- Authority / Receiving Office
- DE · DE
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2022-06-17
- Publication Date
- 2025-09-24
- Estimated Expiration
- 2042-06-17
AI Technical Summary
Existing machine learning models for speech recognition, particularly those using conditional neural networks, are inefficient and resource-intensive, making them unsuitable for deployment on resource-constrained devices due to increased model size, dynamic memory, and latency.
A method that trains a first neural network to learn personalized weights for a second neural network based on conditioning vectors, eliminating the need for concatenation and reducing model size, dynamic memory, and latency by dynamically assigning weights and biases tailored to individual users and device constraints.
This approach results in smaller, more efficient machine learning models suitable for resource-constrained devices with improved performance and reduced latency, enabling personalized speech recognition on devices like smartphones and IoT devices.