Multimodal Input Modality Ranking and Weighting
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Solution Overview
Problem
Existing multi-modal input systems face challenges in accurately distinguishing between intended input modalities, particularly when noisy environments cause confusion between speech recognition and DTMF tone input, leading to user frustration and application misalignment.
Innovation Solution
Ranking input modalities by reliability and using a weighting mechanism to prioritize and process recognition results, ensuring that the most reliable modality's results are used, thereby minimizing misinterpretation and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech recognition and DTMF recognition are activated simultaneously to allow user flexibility in input modality selection, then adaptability is improved, but reliability deteriorates due to interference between modalities in noisy environments
Solution Approach 1:
The system performs preliminary detection of the user's intended input modality before full recognition processing begins. By detecting early signals (such as initial speech patterns or keypad press sequences), the system can pre-determine which modality the user intends to use and activate only that recognition process, preventing interference from the other modality while maintaining user flexibility
Solution Approach 2:
The system introduces an intermediary detection mechanism that sits between the user input and the recognition engines. This intermediary layer analyzes incoming signals to determine the intended modality and routes them to the appropriate recognition process, effectively mediating between multiple activated modalities and preventing them from interfering with each other
2Adaptability or versatility
If the system processes all recognition results from multiple modalities equally, then adaptability is maintained, but measurement precision deteriorates due to noisy environment interference
Solution Approach 1:
The system applies different processing qualities to different modalities based on local conditions. When noise is detected in the audio environment, the system reduces the weight or disables speech recognition processing while maintaining full processing for DTMF tones, creating locally optimized processing quality for each modality based on environmental conditions
Solution Approach 2:
The system dynamically changes processing parameters such as confidence thresholds, weighting factors, and activation levels for different recognition modalities based on environmental analysis. In noisy conditions, the system adjusts parameters to favor non-audio modalities, thereby maintaining measurement precision while preserving adaptability
3Ease of operation
If speech recognition is used in noisy environments to maintain ease of operation, then ease of operation is improved, but reliability deteriorates due to background interference
Solution Approach 1:
The system dynamically adjusts the availability and sensitivity of speech recognition based on environmental noise levels. In quiet environments, speech recognition remains fully active for hands-free operation. In noisy environments, the system dynamically reduces or disables speech recognition while offering alternative input methods, thereby maintaining ease of operation when reliable and preventing reliability deterioration when conditions are poor
Data Source
AI summary
Aspects of the present invention provide for ranking various input modalities relative to each other and processing recognition results received through these input modalities based in part on the ranking.