Multimedia Device Voice Command Contextual Adaptation
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Solution Overview
Problem
Existing speech recognition systems primarily focus on accurately recognizing spoken content without considering the user's intention or context, such as time and ambient environment, and lack adaptability to specific applications on multimedia devices like smart TVs and mobile phones.
Innovation Solution
The system enhances speech recognition by incorporating contextual information, including the application being executed and ambient environment, to provide tailored services by analyzing user speech through natural language processing and image recognition, combining feedback from multiple servers to accurately determine user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems focus only on accurately recognizing spoken content, then recognition accuracy is improved, but adaptability to different applications and environments deteriorates
Solution Approach 1:
The system segments the speech recognition process into multiple independent modules: speech signal processing module, feature extraction module, application identification module, and environment recognition module. Each module handles a specific aspect of the recognition task, allowing the system to maintain high accuracy in speech recognition while simultaneously adapting to different applications and environments through modular processing.
Solution Approach 2:
The system implements a universal speech recognition framework that can function across multiple applications and environments. By integrating application identification and environment recognition capabilities into the core speech recognition system, a single unified system can serve multiple purposes: accurate speech transcription, application-specific processing, and environment-aware adaptation, thereby achieving both precision and versatility.
2Adaptability or versatility
If contextual information and multiple processing modules are added to detect user intention, then adaptability and intention detection are improved, but device complexity increases
Solution Approach 1:
The system divides the complex intention detection task into segmented processing stages: speech signal acquisition, feature extraction, application context analysis, environment context analysis, and intention synthesis. Each stage processes specific information independently and passes results to the next stage, reducing overall system complexity while maintaining comprehensive intention detection capability.
Solution Approach 2:
The system introduces intermediary modules that bridge different processing components: a context integration module that combines application and environment information, and an intention inference module that synthesizes final user intent. These intermediaries manage the complexity by providing structured interfaces between components and coordinating information flow without requiring direct complex interactions between all modules.
3Adaptability or versatility
If speech recognition service is optimized for each specific application, then service relevance is improved, but system complexity and difficulty of implementation increase
Solution Approach 1:
The system implements a universal speech recognition platform that automatically adapts to different applications through a standardized interface. The application identification module detects the current application context and automatically configures processing parameters, eliminating the need for separate implementations for each application while still providing application-specific optimization. This approach maintains service relevance across diverse applications while simplifying implementation through a single unified system.
Solution Approach 2:
The system optimizes speech recognition for different applications by dynamically changing processing parameters rather than implementing different systems. The application context information is used to adjust features such as vocabulary lists, grammar rules, and processing thresholds. This parameter-based adaptation allows the same core system to be optimized for various applications without increasing structural complexity or implementation difficulty.
Data Source
AI summary
The present invention discloses a multimedia device capable of processing a recognized speech-based command. In one embodiment, the device may include a memory to store at least one application therein; an application manager for executing any of the at least one application stored in the memory; and a controller configured to receive from the application manager a list of at least one recognized speech-based command that can be executed by the executed application, wherein the controller is configured: to control a network interface module to transmit any speech-based data received from an outside and the list to the server; and to control the executed application or execute a function non-specific to the currently-executed application, based on a feedback result value received from the server via the network interface module.