Conversational Intent Detection From Ambient Speech Without Wake Words
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automated agents require wake words for activation, leading to disrupted natural conversation flow, false activations, and inaccuracies in noisy environments, especially in multi-speaker scenarios.
Innovation Solution
Utilizing Large Language Models (LLMs) to infer user intent from continuous ambient audio without wake words, incorporating advanced speech recognition and pause detection, speaker diarization, and contextual analysis to enhance accuracy and natural interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wake words are used for activation, then the automated agent can be triggered, but false activations occur and natural conversation flow is disrupted
Solution Approach 1:
The patent removes the wake word requirement entirely from the system. The automated agent continuously monitors audio and uses intent detection algorithms to determine when user speech is directed at the agent, eliminating the need for wake words and thus preserving natural conversation flow while maintaining reliable activation through contextual analysis
Solution Approach 2:
The system changes the activation parameter from keyword-based (wake word detection) to intent-based (semantic analysis). By analyzing speech patterns, context, and user behavior, the system detects when speech is intended for the agent without requiring specific trigger words, thereby improving both reliability and naturalness
2Measurement precision
If wake word detection is used, then activation can be detected, but inaccuracies occur in noisy environments and multi-speaker scenarios
Solution Approach 1:
The patent introduces an intent detection intermediary layer between audio input and agent activation. This layer analyzes speech patterns, contextual cues, and user behavior to determine whether speech is directed at the agent, filtering out false positives from noisy environments and multi-speaker scenarios while improving detection accuracy
Solution Approach 2:
The system continuously monitors user interactions and adjusts its intent detection thresholds based on learned patterns. By analyzing successful and unsuccessful activation cases, the system refines its understanding of directed speech versus ambient conversation, improving accuracy in noisy and multi-speaker environments over time
Data Source
AI summary
The present disclosure relates to systems and methods for enhancing the interaction between users and automated agents, such as digital assistants, by employing Large Language Models (LLMs) to infer the intent of spoken language. The invention involves continuously monitoring ambient audio, converting speech to text, and utilizing LLMs to determine whether spoken language is intended for the automated agent. A structured prompt, including the converted text and specific instructions, is sent to the LLM, which is fine-tuned to process domain-specific prompts. The LLM provides a structured output in a standardized format, indicating the user's intent. The system may involve multiple prompts to perform separate tasks, such as identifying intent and generating additional context-specific data. This approach facilitates a more natural and intuitive user experience by eliminating the need for wake words and allowing seamless conversational interaction with virtual assistants across various platforms and devices.