Speech Recognition Processing Engine Segmentation for Multi-User Domain Adaptation
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Solution Overview
Problem
Conventional speech recognition systems lack user-specific domain support, leading to reduced convenience and performance, especially when multiple users attempt to issue voice commands simultaneously, as they fail to differentiate between supported and unsupported domains and restrict functionality based on user roles.
Innovation Solution
A user-specific database (DB) is configured to store domain and content provider information, allowing for personalized speech recognition processing, with separate functions for drivers and passengers, and enabling simultaneous processing of multiple users' commands using multiple processing engines, while handling unsupported domains as exceptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single speech recognition system processes all domains for all users, then system simplicity is maintained, but user-specific customization and convenience are reduced
Solution Approach 1:
The system is segmented into multiple processing engines (first processing engine for first user, second processing engine for second user) that operate independently. Each engine has its own speech recognition module and can process speech commands simultaneously without interfering with others, enabling user-specific customization while maintaining overall system functionality.
Solution Approach 2:
The speech recognition system is designed to handle multiple domains (navigation, music, messaging, etc.) and multiple users through a universal architecture. The processing module can dynamically select appropriate content providers based on domain and user preferences stored in the database, allowing one system to serve multiple purposes and users effectively.
2Productivity
If multiple processing engines are executed to process simultaneous speech commands from multiple users, then speech recognition performance and user convenience are improved, but system complexity and resource consumption increase
Solution Approach 1:
The processing capability is divided into separate processing engines for different users. Each engine independently processes speech commands from its associated user, enabling simultaneous processing without conflict. This segmentation allows the system to handle multiple users' commands concurrently, improving overall productivity.
Solution Approach 2:
A database acts as an intermediary between multiple users and the processing engines. It stores and manages user-specific information, domain preferences, and content provider mappings, allowing processing engines to efficiently retrieve necessary data without direct complex interactions between engines themselves.
3Ease of operation
If domain support is uniformly applied to all users, then system simplicity is maintained, but user preference customization and satisfaction are reduced
Solution Approach 1:
The system implements local quality by storing user-specific domain preferences and content provider mappings in individual user records within the database. Each user can have customized domain support and content provider associations, allowing the system to adapt to local user needs and preferences rather than applying uniform settings to all users.
Solution Approach 2:
User preferences, domain information, and content provider mappings are pre-stored in the database before speech recognition processing occurs. This preliminary action allows processing engines to quickly retrieve and apply user-specific configurations during speech command processing, enhancing user convenience without adding complexity to the real-time processing flow.
4Reliability
If speech recognition processes all voice commands uniformly, then processing consistency is maintained, but role-based access control (driver vs. passenger) and safety are reduced
Solution Approach 1:
The system segments user verification and role assignment by creating separate processing engines for different user roles (driver, passenger). Each engine applies role-specific rules and restrictions when processing speech commands, ensuring that driver and passenger have appropriate access controls based on their roles while maintaining processing consistency within each role.
Solution Approach 2:
The database serves as an intermediary that stores and verifies user identity and role information. Before processing speech commands, the system queries the database to confirm user role (driver or passenger), allowing role-based access control to be implemented without adding complex verification logic to the speech recognition processing itself.
Data Source
AI summary
Apparatus and method for processing speech recognition may include a speech recognition module that recognizes a voice uttered from a user, and a processing module that calls a user DB where information associated with the user is registered when a voice command of the user is input by the speech recognition module, verifies setting information related to a domain corresponding to the voice command, and processes the voice command through a content provider linked to the associated domain.