Multimodal Utterance Resolution for Ambiguous In-Vehicle Commands
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Solution Overview
Problem
Existing in-vehicle electronic assistant systems struggle to interpret vocal utterances with ambiguity, despite accurate speech recognition, due to insufficient understanding of the meaning and intent of the utterance.
Innovation Solution
A multimodal input system that employs natural language processing and contextual disambiguation to resolve ambiguities in vocal utterances by utilizing various context factors, including previous interactions, vehicle status, and gaze data, to determine the intent and object of the command.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is used to convert vocal utterances to words, then the conversion accuracy is improved, but the understanding of meaning and intent deteriorates
Solution Approach 1:
The patent transitions from single-modal speech recognition to multi-modal input processing by incorporating gaze data, haptic input, and contextual information as additional dimensions. This allows the system to resolve ambiguities that speech recognition alone cannot address, thereby recovering the lost meaning and intent information while maintaining high speech recognition accuracy.
Solution Approach 2:
The patent introduces contextual information as an intermediary layer between speech recognition and intent understanding. By incorporating previous interactions, vehicle status, and environmental context, the system bridges the gap between accurate word conversion and meaningful intent interpretation, allowing it to disambiguate utterances that would otherwise be misunderstood.
2Ease of operation
If multiple input modes are accepted for driver assistance, then the ease of operation is improved, but the complexity of the system deteriorates
Solution Approach 1:
The patent implements a unified processing framework that handles multiple input modes (speech, gaze, haptic, text) through a single natural language processing system. This universal approach allows the system to accept diverse inputs without requiring separate processing pipelines for each mode, thereby maintaining ease of operation while managing system complexity through consolidation rather than proliferation of components.
Solution Approach 2:
The patent segments the multimodal processing into distinct hierarchical levels: input acquisition, contextualization, disambiguation, and execution. By organizing the complex multimodal system into modular segments with clear interfaces, the system maintains ease of operation through consistent user experience while managing complexity through structured organization of processing stages.
3Measurement precision
If contextual information is used to disambiguate utterances, then the accuracy of intent determination is improved, but the processing time deteriorates
Solution Approach 1:
The patent pre-processes and stores contextual information (vehicle status, previous interactions, environmental data) in ready-to-use formats before utterances occur. This preliminary preparation allows the system to quickly retrieve and apply relevant context during utterance disambiguation without performing extensive real-time analysis, thereby maintaining high intent determination accuracy while minimizing additional processing time.
Solution Approach 2:
The patent applies contextual disambiguation selectively based on the ambiguity level of the utterance. For clear, unambiguous commands, the system processes them quickly with minimal contextual analysis. For ambiguous utterances, it applies full contextual disambiguation. This partial application approach ensures high accuracy for problematic cases while avoiding unnecessary processing time for clear commands, thus balancing accuracy and speed.
Data Source
AI summary
A system and method of responding to a vocal utterance may include capturing and converting the utterance to word(s) using a language processing method, such as natural language processing. The context of the utterance and of the system, which may include multimodal inputs, may be used to determine the meaning and intent of the words.