AI Speech Recognition Without Wake-Up Word
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Solution Overview
Problem
Current speech recognition technologies require a wake-up word to initiate interaction, limiting user interaction with machines unless the word is explicitly uttered, and fail to facilitate natural conversation without pre-defined triggers.
Innovation Solution
An information providing method and device that gathers situational information from user behavior through home monitoring devices and electronic devices, generates spoken sentences, and converts them into spoken utterance information to initiate and sustain conversations without the need for a wake-up word, using deep neural networks and feedback analysis to improve interaction naturalness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If speech recognition is constantly activated to enable natural interaction, then user interaction capability is improved, but power consumption and processing resource usage increase excessively
Solution Approach 1:
The system performs preliminary analysis by extracting features from user utterances and comparing them against predefined wake-up word features before fully activating speech recognition. This preliminary filtering action enables the system to prepare for potential interaction without committing full computational resources, thus maintaining ease of operation while controlling power consumption.
Solution Approach 2:
The speech recognition process is segmented into multiple stages: initial feature extraction, wake-up word detection, and full speech recognition activation. By dividing the continuous speech processing into discrete segments triggered by specific conditions, the system achieves natural interaction capability only when needed, significantly reducing overall power consumption while maintaining user interaction effectiveness.
2Use of energy by moving object
If speech recognition is activated only by wake-up word to save power, then power consumption is reduced, but user interaction is limited and less natural
Solution Approach 1:
The system dynamically adjusts its speech recognition activation strategy based on real-time analysis of user utterances. Instead of static wake-up word triggering, the system continuously evaluates speech features and adapts its activation threshold, enabling more flexible and natural user interaction while maintaining power efficiency through condition-based activation rather than constant operation.
Solution Approach 2:
The system changes the activation parameter from fixed wake-up word matching to dynamic feature-based detection. By monitoring speech features such as phoneme patterns, pitch contours, and temporal characteristics, the system adjusts its activation criteria to recognize diverse user intents beyond predefined wake-up words, thereby improving interaction flexibility while maintaining power consumption benefits.
3Reliability
If wake-up word recognition is used to trigger speech recognition, then system activation is controlled, but continuous conversation without wake-up word is not enabled
Solution Approach 1:
The system maintains continuous monitoring of speech features even in idle states, enabling seamless conversation flow without requiring repeated wake-up words. The useful action of speech feature analysis continues at a low computational level, allowing the system to detect and respond to user speech continuously while maintaining reliable activation control through configured thresholds and state management.
Solution Approach 2:
The system implements feedback mechanisms where the outcome of each speech recognition cycle influences subsequent activation behavior. When conversation context is detected, the system adjusts its activation sensitivity to maintain continuous interaction. This feedback loop enables the system to remember conversation state and facilitate natural dialogue continuity while preserving reliable activation control through adaptive threshold adjustment based on contextual feedback.
Data Source
AI summary
Disclosed are an information providing device and an information providing method, which provide information enabling a conversation with a user by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for Internet-of-Things. An information providing method according to one embodiment of the present disclosure includes gathering first situational information from a home monitoring device, gathering, from the first electronic device, second situational information corresponding to the first situational information, gathering, from the home monitoring device, third situational information containing a behavioral change of the user after gathering the first situational information, generating a spoken sentence to provide to the user on the basis of the first situational information to the third situational information, and converting the spoken sentence to spoken utterance information to be output to the user.