Multi-User Dialog Management via Speech Direction Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current speech recognition systems struggle to effectively manage and coordinate natural language dialogues involving multiple users, often requiring repetitive wake words and failing to distinguish between user-to-user speech and system-directed speech, leading to inefficient and user-unfriendly interactions.
Innovation Solution
A virtual assistant system that utilizes automatic speech recognition (ASR), natural language understanding (NLU), and computer vision to identify and respond to multiple users, determining when speech is directed towards the system or another user, and adjusting its responses accordingly, allowing for seamless multi-user dialog management and contextual understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system processes all speech inputs in multi-user scenarios, then it can respond to system-directed speech, but it cannot distinguish between user-to-user speech and system-directed speech
Solution Approach 1:
The system segments the multi-user dialogue into separate user contexts, tracking which user is speaking to which user. This allows the system to identify system-directed speech versus user-to-user speech by analyzing the directional flow of conversation between segmented user participants.
Solution Approach 2:
The system introduces an intermediary analysis layer that examines dialogue patterns, turn-taking sequences, and contextual cues to determine speech direction. This intermediary process acts as a mediator between raw speech inputs and system responses, identifying whether speech is directed at the system or another user.
2Measurement precision
If the system requires wake words for each user input, then it can accurately identify system-directed speech, but it requires repetitive wake words and creates inefficient interactions
Solution Approach 1:
The system performs preliminary analysis of dialogue context, user turns, and conversational flow before determining whether speech is system-directed. This preliminary action allows the system to pre-identify system-directed speech without requiring explicit wake words, improving interaction efficiency while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts its speech direction detection based on the current dialogue state, user participation patterns, and contextual cues. This dynamic approach allows the system to flexibly identify system-directed speech in varying multi-user scenarios without relying on static wake word requirements.
3Speed
If the system responds to all user speech in multi-user scenarios, then it can be highly responsive, but it creates unnatural and disruptive interruptions in user-to-user conversations
Solution Approach 1:
The system applies partial action by selectively responding only to system-directed speech while allowing user-to-user speech to proceed without system intervention. This partial response strategy maintains natural conversation flow for user interactions while providing timely responses to system-directed queries.
Solution Approach 2:
The system uses feedback from dialogue analysis to determine appropriate response timing and targeting. By continuously monitoring conversation patterns and user engagement, the system receives feedback that helps it distinguish when to interject and when to remain silent, maintaining natural conversation flow while staying responsive.
Data Source
AI summary
A natural language system may be configured to act as a participant in a conversation between two users. The system may determine when a user expression such as speech, a gesture, or the like is directed from one user to the other. The system may processing input data related the expression (such as audio data, input data, language processing result data, conversation context data, etc.) to determine if the system should interject a response to the user-to-user expression. If so, the system may process the input data to determine a response and output it. The system may track that response as part of the data related to the ongoing conversation.