Federated Acoustic Model Refinement for Voice Ordering
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Solution Overview
Problem
The restaurant industry faces challenges in adapting to post-pandemic dining habits, with patrons missing the ease of online ordering and restaurants needing improved systems for enhanced service and user experience.
Innovation Solution
A federated, context-sensitive language model system comprising a server and edge devices that use machine learning models to adapt to user-specific contexts, enabling speech recognition and order processing, with continuous learning and updates to improve language and intent recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If centralized machine learning models are distributed to edge devices for local operation, then speech recognition speed and responsiveness are improved, but model accuracy and adaptability may deteriorate due to limited local computing resources and data
Solution Approach 1:
The system divides the machine learning model into two parts: a core model that remains on the centralized server and a lightweight local model that runs on edge devices. The local model handles immediate speech recognition tasks for fast responsiveness, while the server model periodically receives updated training data from multiple edge devices to maintain high accuracy. This segmentation allows both speed and reliability to coexist.
Solution Approach 2:
The system implements a feedback mechanism where speech recognition results and contextual data from edge devices are sent back to the centralized server. The server uses this feedback to continuously retrain and update the core model, which then distributes improved model parameters back to edge devices. This closed-loop feedback ensures the local models remain accurate despite operating with limited local data.
2Adaptability or versatility
If local language models are trained using user-specific context data on edge devices, then user-specific adaptability is improved, but system complexity and data management overhead increase
Solution Approach 1:
The system uses a universal centralized server that performs multiple functions: storing core model parameters, collecting contextual data from multiple users, training updated models, and distributing updates to all edge devices. This universal server approach allows individual edge devices to maintain simple, user-adapted local models while benefiting from the complex data aggregation and model training performed centrally, thus achieving user-specific adaptability without proportionally increasing device complexity.
3Measurement precision
If context data is continuously collected and processed to update language models, then speech recognition accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
Instead of continuously processing and updating models in real-time, the system implements periodic model updates. Context data is collected from edge devices over time, and the centralized server performs model training and updates at scheduled intervals. This periodic approach allows the system to accumulate sufficient data for accurate model training without the continuous computational overhead, thus improving speech recognition accuracy while minimizing data processing time impact on user experience.
Data Source
AI summary
A system and method for federated, context-sensitive, acoustic model refinement comprising a federated language model server and a plurality of edge devices. The federated language model server may comprise one or more machine learning models trained and developed centrally on the server, and distribute these one or more machine learning models to edge devices wherein they may be operated locally on the edge devices. The edge devices may gather or generate context data that can be used by a speech recognition engine, and the local language models contained therein, to develop adaptive, context-sensitive, user-specific language models. Periodically, the federated language model server may select a subset of edge devices from which to receive uploaded local model parameters, that may be aggregated to perform central model updates wherein the updated model parameters may then be sent back to edge devices in order to update the local model parameters.