Voice Control Disambiguation via Feedback Model
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Solution Overview
Problem
Voice-based control systems in e-commerce face challenges in accurately interpreting user intentions due to ambiguity and errors in automatic speech recognition and natural language understanding, leading to friction in the purchasing process.
Innovation Solution
A system that uses a feedback model to determine when and how to request additional user input through either voice or graphical interfaces to resolve ambiguities, optimizing for response rate, purchase rate, and customer satisfaction by learning from user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice-based control is used for e-commerce interactions, then user convenience is improved, but ambiguity in user intent increases
Solution Approach 1:
The system implements feedback loops where the digital assistant seeks clarification from users when intent ambiguity is detected. The system provides feedback about possible interpretations and receives user confirmation or correction, thereby resolving ambiguity while maintaining voice-based convenience.
Solution Approach 2:
The system introduces an intermediary clarification mechanism between voice input and action execution. When ambiguity is detected, the system acts as a mediator by presenting multiple interpretations to the user and selecting the correct one based on user feedback, thus bridging the gap between voice convenience and intent accuracy.
2Measurement precision
If additional user input is requested to resolve ambiguity, then user intent accuracy is improved, but interaction complexity increases
Solution Approach 1:
The system applies partial action by requesting only the minimum necessary additional input required to resolve ambiguity. Rather than requiring complete rephrasing or multiple questions, the system asks for just enough clarification to disambiguate intent, thereby improving accuracy without excessive interaction complexity.
Solution Approach 2:
The system changes the parameter of interaction modality dynamically. Based on the detected ambiguity, the system switches between voice-only mode and voice-plus-confirmation mode, adjusting the level of additional input required based on the severity and type of ambiguity detected.
3Measurement precision
If multiple possible interpretations are considered, then user intent accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of voice input to detect potential ambiguities before final interpretation. By identifying ambiguity early in the processing pipeline, the system can prepare clarification questions in advance and resolve intent faster, rather than discovering multiple interpretations later in the conversation flow.
Solution Approach 2:
The system dynamically adjusts its interpretation strategy based on detected ambiguity. When ambiguity is detected, the system transitions from direct interpretation to clarification-seeking mode. This dynamic adaptation allows the system to consider multiple interpretations only when necessary, minimizing processing time for unambiguous inputs while maintaining accuracy for ambiguous ones.
Data Source
AI summary
In various embodiments, a voice command is associated with a plurality of processing steps to be performed. The plurality of processing steps may include analysis of audio data using automatic speech recognition, generating and selecting a search query from the utterance text, and conducting a search of database of items using a search query. The plurality of processing steps may include additional or different steps, depending on the type of the request. In performing one or more of these processing steps, an error or ambiguity may be detected. An error or ambiguity may either halt the processing step or create more than one path of actions. A model may be used to determine if and how to request additional user input to attempt to resolve the error or ambiguity. The voice-enabled device or a second client device is then causing to output a request for the additional user input.