Semantic Carrier Phrase Prediction for Speech Input
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Solution Overview
Problem
Traditional speech-input devices require exact matches with predefined carrier phrases, limiting user interaction and frustrating users who speak phrases with similar meanings but not exact matches, as they do not trigger intended actions.
Innovation Solution
A system that predicts and learns user-intended actions by comparing speech inputs to a list of known carrier phrases, using semantic similarity and user activity monitoring to update relevance scores and dynamically add new phrases to the list, allowing for flexible recognition and execution of commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predefined carrier phrases are used for speech recognition, then the device can recognize exact matches and perform corresponding actions, but users become frustrated when speaking phrases with similar meanings that do not exactly match the predefined phrases
Solution Approach 1:
The system changes the parameter of phrase matching from exact string equality to semantic similarity based on relevance scores. The carrier phrase module compares spoken phrases to known carrier phrases using relevance scoring that evaluates semantic similarity rather than requiring exact matches, allowing phrases like 'Navigate me to' to match the carrier phrase 'Navigate to'.
Solution Approach 2:
The system dynamically adapts the carrier phrase list based on user interactions. The carrier phrase application monitors user activity and updates the list of known carrier phrases over time, making the recognition system flexible and evolving rather than static and rigid.
2Reliability
If a list of predefined carrier phrases is maintained, then the device can reliably trigger intended actions, but users must learn and memorize these exact phrases to use the device effectively
Solution Approach 1:
The system performs self-learning by automatically monitoring user interactions and updating the carrier phrase list without requiring explicit user input or programming. The carrier phrase application learns from observed user behavior patterns and autonomously expands its recognition capabilities, eliminating the need for users to manually learn predefined phrases.
Solution Approach 2:
The system implements feedback loops where user interactions with the device are monitored and fed back to update the carrier phrase relevance scores and expand the known carrier phrases list. This continuous feedback mechanism allows the system to adapt to user preferences and improve recognition accuracy over time.
3Productivity
If exact match recognition is used, then the system maintains simple and fast processing, but the range of recognized commands remains limited to predefined phrases only
Solution Approach 1:
The carrier phrase module serves multiple functions: it performs exact matching for quick recognition, semantic similarity assessment for flexible interpretation, and dynamic learning for continuous improvement. This multi-functionality allows the system to maintain fast processing while expanding recognition capabilities beyond strict predefined phrases.
Data Source
AI summary
Predicting and learning users' intended actions on an electronic device based on free-form speech input. Users' actions can be monitored to develop a list of carrier phrases having one or more actions that correspond to the carrier phrases. A user can speak a command into a device to initiate an action. The spoken command can be parsed and compared to a list of carrier phrases. If the spoken command matches one of the known carrier phrases, the corresponding action(s) can be presented to the user for selection. If the spoken command does not match one of the known carrier phrases, search results (e.g., Internet search results) corresponding to the spoken command can be presented to the user. The actions of the user in response to the presented action(s) and/or the search results can be monitored to update the list of carrier phrases.