Multi-Modal IME With Server Speech Recognition for Mobile Input
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computer users, particularly those using mobile devices, face challenges with limited keyboard usability and processing power constraints, making speech-to-text conversion inefficient and requiring user-specific training.
Innovation Solution
A multi-modal input method editor (IME) that integrates speech-to-text functionality, allowing users to switch between typed and spoken input, utilizing context-specific language models and remote server processing to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech-to-text conversion is implemented on mobile devices, then input accessibility is improved, but processing power requirements increase
Solution Approach 1:
The system segments speech processing into two parts: local device processing for audio capture and preliminary processing, and remote server processing for complex speech-to-text conversion. This allows the mobile device to provide accessibility benefits while offloading heavy computational tasks to remote servers with sufficient processing power.
Solution Approach 2:
A remote server acts as an intermediary between the mobile device and the speech recognition system. The server receives audio data from the device, performs comprehensive speech-to-text conversion using powerful processing resources, and returns results to the device, enabling sophisticated functionality on devices with limited processing power.
2Measurement precision
If user-specific training is required for speech recognition, then recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by capturing audio data and extracting features locally on the device before sending to the server. This preliminary processing prepares the data in advance, allowing the server to perform more efficient speech recognition without requiring complex real-time training for each user.
Solution Approach 2:
Instead of training a unique speech recognition model for each user, the system uses a universal server-based recognition system that processes audio data from multiple users. The server maintains a database of user-specific information and context, copying relevant patterns to achieve high accuracy without requiring complex client-side training.
3Volume of moving object
If keyboard size is reduced for mobile devices, then device portability is improved, but input accuracy deteriorates
Solution Approach 1:
The system replaces the mechanical keyboard input method with acoustic field-based speech recognition. Instead of relying on physical key presses that are difficult on small screens, users speak their input and the system converts audio signals to text, achieving high input accuracy without requiring a large physical keyboard.
Solution Approach 2:
The input method editor provides universal functionality by accepting multiple input modalities (typed text, spoken words, and combinations) and converting them all into standard text output for applications. This multi-functional approach ensures input accuracy regardless of the user's physical situation or device constraints.
4Speed
If speech-to-text processing is performed locally on device, then processing speed is improved, but processing power requirements increase
Solution Approach 1:
The system uses periodic action by capturing audio in continuous streams and processing it in manageable chunks or phrases. The device continuously monitors for speech input, processes completed phrases when ready, and maintains readiness for the next phrase, achieving efficient processing speed without requiring sustained high processing power.
Solution Approach 2:
The device performs partial processing actions locally (audio capture, basic audio processing, feature extraction) and sends the results to the server for the excessive or comprehensive action (full speech-to-text conversion). This allows the device to maintain processing speed for basic operations while relying on server power for comprehensive conversion.
Data Source
AI summary
A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.