Adaptive Speech Detection for Crosstalk and Privacy Control
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Solution Overview
Problem
In large venues with multiple microphones, crosstalk and computational resource issues arise due to simultaneous speech capture, and conventional push-to-talk methods incur errors and privacy concerns.
Innovation Solution
A situationally adaptive speech detection system that continuously processes audio and uses machine learning to determine relevant speech, minimizing crosstalk and computational resources while protecting privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple microphones are activated simultaneously to capture speech in large venues, then speech capture coverage is improved, but crosstalk between channels and computational costs increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring audio streams and detecting speech onset before full speech capture begins. This allows the system to prepare and activate specific microphones in advance, ensuring optimal coverage while minimizing unnecessary microphone activation and associated crosstalk.
Solution Approach 2:
The system extracts and processes only the relevant audio streams that contain actual speech content, separating them from background noise and irrelevant conversations. By identifying and isolating speech-bearing streams through continuous audio analysis, the system activates only the necessary microphones, reducing crosstalk and computational overhead.
2Reliability
If push-to-talk is used to control speech transmission, then privacy and relevance filtering are improved, but human errors in button timing occur
Solution Approach 1:
The system performs self-service by automatically detecting speech onset and offset through continuous audio stream analysis, eliminating the need for manual push-to-talk button operation. The system autonomously determines when speech begins and ends, activating microphones and processing audio without human intervention, thereby achieving both privacy protection and precise timing without human error.
Solution Approach 2:
The system implements feedback by continuously monitoring audio streams and using real-time speech detection to control microphone activation. The continuous audio analysis provides feedback about speech presence, allowing the system to dynamically adjust microphone states based on actual speech content rather than relying on imperfect manual button pressing.
3Measurement precision
If all microphones remain active continuously, then speech detection accuracy is improved, but computational resources and API usage costs increase
Solution Approach 1:
The system employs periodic action by continuously analyzing audio streams at regular intervals to detect speech onset. This continuous periodic monitoring allows the system to maintain high speech detection accuracy while activating microphones only when speech is detected, rather than keeping all microphones continuously active, thereby reducing computational resource consumption and API usage costs.
Solution Approach 2:
The system applies dynamics by dynamically adjusting microphone activation states based on real-time speech detection results. Microphones are activated only when and where speech is detected, rather than maintaining a static all-on configuration. This dynamic approach preserves speech detection accuracy while significantly reducing the computational burden of processing audio from all microphones simultaneously.
Data Source
AI summary
A system includes a hardware processor and a memory storing software code and a natural language understanding (NLU) machine learning model. The hardware processor executes the software code to determine the proximity of a human in a venue to a microphone communicatively coupled to the system, activate the microphone before a start of speech by the human, in response to determining the proximity of the human being within a predetermined distance from the microphone, and detect an action by the human signifying an end of the speech. The software code is further executed to deactivate the microphone upon detecting the action to provide an audio recording including the speech, the audio recording beginning before the start of the speech and terminating at the end of the speech, determine, using the NLU machine learning model, that the speech includes an unwanted portion, and erase the unwanted portion of the audio recording.