Conversation Recovery via Semantic Alternative Generation
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Solution Overview
Problem
Speech recognition systems often fail to respond effectively to user inputs that vary from expected or preprogrammed utterances, leading to errors and an unsatisfactory user experience, as they struggle to process slight variations in commands or unclear audio inputs.
Innovation Solution
The system implements a conversation recovery mechanism that generates alternative text representations semantically similar to the user's input by analyzing past utterances and developer-generated data, allowing it to suggest alternative commands and confirm with the user before proceeding, thus reducing errors and improving interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the speech recognition system uses preprogrammed expected utterances for command recognition, then it can reliably identify standard commands, but it fails to respond effectively to variations from expected utterances
Solution Approach 1:
The system pre-generates alternative utterance representations and stores them in a database before runtime. When a command is received, the system queries this pre-prepared database of alternatives rather than generating options in real-time, enabling fast response to utterance variations while maintaining reliable recognition of standard commands
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between the speech recognition input and the command execution. This intermediary queries a database of alternative representations to find matching commands, allowing the system to handle utterance variations without directly modifying the core speech recognition or command execution modules
2Adaptability or versatility
If the system generates multiple alternative text representations to handle input variations, then it improves adaptability to different utterances, but it increases processing time and complexity
Solution Approach 1:
The system performs the computationally intensive generation of alternative text representations in advance and stores them in a database. During runtime, it only needs to query and compare against pre-generated alternatives, dramatically reducing processing time while maintaining high adaptability to input variations
Solution Approach 2:
The system extracts only the essential alternative representations needed for command recognition from the full set of possible utterance variations. By selecting and storing only the most relevant alternatives in the database, it reduces the amount of data to process during runtime while maintaining effective handling of common input variations
3Measurement precision
If the system queries a database of past utterances and developer-generated data for alternatives, then it improves accuracy of alternative suggestions, but it increases device complexity and data storage requirements
Solution Approach 1:
The system introduces a database intermediary layer that stores pre-processed alternative representations from past utterances and developer-generated data. This intermediary database handles the complexity of data storage and retrieval, allowing the main speech recognition system to query accurate alternatives without directly managing the complexity of data storage and processing
Solution Approach 2:
The system pre-processes and stores alternative representations from past utterances and developer data before runtime. This preliminary preparation of accurate alternatives in the database reduces the computational burden during runtime while maintaining high accuracy in alternative suggestions
Data Source
AI summary
Exemplary embodiments relate to a system for recovering a conversation between a user and the system when the system is unable to properly respond to a user's input. The system may process the user input and determine an error condition exists. The system may query one or more storage systems to identify candidate text data based on their semantic similarity to the user input. The storage systems may store data related to past frequently entered inputs and/or user-generated inputs. Alternative text data is selected from the candidate text data, and presented to the user for confirmation.