Acoustic Environment Classification for Natural Voice Wake-Up
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Solution Overview
Problem
Existing voice-triggered wake-up systems for devices require a specific trigger phrase, leading to less natural user-device interaction and potentially degrading user experience due to the need for repeated phrase usage.
Innovation Solution
A data processing system that classifies the acoustic environment based on the direct to reverberant ratio (DRR) of audio signals, allowing for control states that determine whether a trigger word is required for accessing subsequent processing functions like speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trigger phrase is required for voice-activated wake-up, then false activation from background noise is prevented, but user interaction becomes less natural and requires repeated phrase usage
Solution Approach 1:
The system applies different wake-up mechanisms to different acoustic environments. In close-talk scenarios (local condition), no trigger phrase is required, while in far-field scenarios, a trigger phrase is required. This local differentiation resolves the contradiction by tailoring the wake-up behavior to the specific acoustic context.
Solution Approach 2:
The system dynamically adjusts the wake-up threshold and trigger requirements based on real-time acoustic environment classification. The classification unit continuously monitors acoustic characteristics and adjusts system behavior accordingly, allowing the wake-up mechanism to be flexible rather than static.
2Speed
If the system continuously listens for voice commands in stand-by mode, then voice commands can be recognized immediately, but power consumption increases
Solution Approach 1:
The system segments the speech processing pipeline into multiple stages with different power requirements. The classification unit and acoustic environment detector operate in a low-power always-on mode, while the full speech recognition system is activated only when needed. This segmentation allows the system to maintain quick response capability while minimizing continuous power consumption.
Solution Approach 2:
The system performs preliminary acoustic environment classification and trigger phrase detection in a low-power state before activating the full speech recognition system. This preliminary action allows the system to prepare for potential voice commands without committing full processing power continuously.
3Adaptability or versatility
If the system activates full speech recognition functionality upon detecting a trigger phrase, then speech commands can be processed, but power consumption increases significantly
Solution Approach 1:
The system dynamically controls the activation of speech recognition functionality based on acoustic environment classification. In close-talk scenarios, the system activates speech recognition more readily, while in far-field scenarios, it maintains stricter trigger requirements. This dynamic adaptation allows the system to balance functionality with power consumption based on real-time conditions.
Data Source
AI summary
The application describe a data processing system and associated methods for processing received speech data. The data processing system comprises:a classification unit configured to receive data derived from an audio signal and, based on the received data, to determine a classification state of an acoustic environment;wherein access to a subsequent processing unit is controlled based on the classification state of the acoustic environment. The classification state may be derived based on a pre-trained model, wherein the representation comprises a representation of the direct to reverberant ratio (DRR) of the audio signal.