Robot Speech Recognition Biasing Using Environmental Cues
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Solution Overview
Problem
Current robotic devices face challenges in accurately interpreting voice commands due to environmental ambiguities, leading to potential misinterpretations and unsafe actions, as they lack context-aware biasing mechanisms to differentiate between similar commands based on their surroundings.
Innovation Solution
A system that uses environmental cues, such as object locations and human interactions, to bias speech recognition scores, ensuring that the most accurate and safe interpretations of voice commands are selected, by adjusting recognition and impact scores to prioritize feasible and safe actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is performed without environmental context biasing, then processing speed is maintained, but transcription accuracy deteriorates due to environmental ambiguities
Solution Approach 1:
The system performs preliminary actions by pre-processing environmental data (images, sensor readings) to identify objects and their locations before speech recognition occurs. This creates a context database that is ready to bias speech transcription, resolving ambiguities faster and improving accuracy without adding significant complexity during the critical speech processing moment.
Solution Approach 2:
The patent introduces an intermediary context processing layer that mediates between raw environmental data and speech recognition results. This intermediary layer analyzes environmental cues and applies biasing to candidate transcriptions, acting as a bridge that improves accuracy while maintaining manageable system complexity through modular architecture.
2Measurement precision
If multiple candidate transcriptions are evaluated with environmental biasing, then transcription accuracy improves, but processing time increases
Solution Approach 1:
The system applies local quality by focusing computational resources on evaluating only those candidate transcriptions that are consistent with the local environmental context. Instead of uniformly processing all candidates, the system biases evaluation toward transcriptions that match detected objects and environmental cues, improving accuracy while reducing overall processing time.
Solution Approach 2:
The patent implements partial action by evaluating a subset of candidate transcriptions that are most likely to be correct based on environmental context. Rather than exhaustively evaluating all possible transcriptions, the system applies environmental biasing to prioritize and evaluate only the most plausible candidates, achieving high accuracy with reduced processing time.
3Reliability
If the robot acts on speech commands without context verification, then response speed is maintained, but safety deteriorates due to misinterpretations
Solution Approach 1:
The system applies preliminary anti-action by pre-evaluating speech commands against environmental context before execution. Environmental cues are analyzed in advance to verify that the intended action is safe and appropriate for the current situation, preventing harmful actions while maintaining response speed through efficient context-checking mechanisms.
Solution Approach 2:
The patent implements feedback by continuously comparing intended actions with environmental context and providing verification before execution. The system uses environmental sensors to monitor the situation and feedback this information to the command interpretation process, ensuring safety while maintaining productivity through streamlined feedback loops.
Data Source
AI summary
Systems and methods are described include a robot and/or an associated computing system that can use various cues about an environment of the robot to apply a bias to increase the accuracy of speech transcription. In some implementations, audio data corresponding to a spoken instruction to a robot is received. Candidate transcriptions of the audio data are obtained. A respective action of the robot corresponding to each of the candidate transcriptions of the audio data is determined. One or more scores indicating characteristics of a potential outcome of performing the respective action corresponding to the candidate transcription of the audio data are determined for each of the candidate transcriptions of the audio data. A particular candidate transcription is selected from among the candidate transcriptions based at least on the one or more scores. The action determined for the particular candidate transcription is performed.