Robot Speech Recognition Biasing Using Environmental Context
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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 speech recognition that considers the robot's location and surrounding objects.
Innovation Solution
A computer-implemented method that uses environmental cues, such as object inventories and location data, to bias speech recognition scores, ensuring that the most accurate and safe interpretations of voice commands are selected, thereby reducing the likelihood of misinterpretation and unsafe outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition is used without environmental context, then the system is simple and fast, but speech transcription accuracy deteriorates due to environmental ambiguities
Solution Approach 1:
The system performs preliminary actions by pre-processing environmental data to create an object inventory and determining the robot's location before speech recognition occurs. This contextual information is prepared in advance and used to bias the speech recognition process, improving accuracy without adding complexity during the actual speech processing step
Solution Approach 2:
The patent introduces an intermediary contextual biasing mechanism that mediates between the raw speech input and the final transcription. The system uses environmental context (object inventory, location data) as an intermediary to adjust speech recognition scores, selecting transcriptions that are consistent with the known environment while filtering out ambiguous or incorrect interpretations
2Measurement precision
If speech recognition scores are adjusted based on environmental context, then transcription accuracy improves, but the processing time increases
Solution Approach 1:
Environmental data processing, object identification, and location determination are performed as preliminary actions before speech recognition. This allows the contextual biasing information to be ready when speech input occurs, minimizing the additional processing time required during actual speech transcription
Solution Approach 2:
The system changes parameters by adjusting speech recognition scores based on environmental context rather than reprocessing the entire speech recognition pipeline. This selective parameter adjustment (modifying transcription probabilities) is computationally efficient and adds minimal processing time while significantly improving accuracy
3Adaptability or versatility
If the robot acts on all candidate transcriptions, then action coverage is complete, but safety deteriorates due to potential misinterpretations
Solution Approach 1:
The contextual biasing mechanism acts as an intermediary safety filter between candidate transcriptions and robot actions. It adjusts scores to favor transcriptions consistent with the environmental context, effectively filtering out potentially harmful misinterpretations while preserving valid action candidates
Solution Approach 2:
The system uses environmental context as feedback to evaluate and rank candidate transcriptions. By comparing each transcription against known environmental facts (object inventory, location), the system provides feedback that eliminates unsafe interpretations and reinforces safe, context-appropriate actions
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.