Speech Signal Processing Using Vibration Cues for Noise-Robust Recognition
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Solution Overview
Problem
Existing speech signal processing systems struggle with robustness in separating user speech from mixed signals that include ambient noise and other non-user speech, limiting the effectiveness of speech recognition and voiceprint authentication.
Innovation Solution
A method that utilizes user-generated vibration and brain wave signals in conjunction with speech signals to enhance voiceprint recognition, leveraging these additional signals to improve confidence levels and suppress noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If voice signal processing is performed without voice activity detection, then processing continuity is maintained, but energy consumption increases and unnecessary processing occurs
Solution Approach 1:
The voice activity detection module performs preliminary detection on the voice signal before the main processing pipeline. By detecting voice activity in advance, the system determines whether subsequent processing operations are necessary, thereby avoiding unnecessary processing and reducing energy consumption while maintaining processing efficiency when voice is present
2Reliability
If traditional voice activity detection methods are used, then voice activity can be detected, but processing delays occur and real-time performance deteriorates
Solution Approach 1:
The voice activity detection is segmented into multiple parallel frequency bands instead of processing the entire spectrum sequentially. The signal is divided into different frequency ranges, and detection is performed simultaneously on each band, which reduces overall processing delay while maintaining detection accuracy through multi-band analysis
Solution Approach 2:
The system performs partial processing by focusing detection efforts only on frequency bands where voice activity is likely to occur, rather than uniformly processing all frequency bands. This selective approach reduces processing time while maintaining reliable detection of actual voice activity
3Productivity
If simple voice activity detection is implemented, then processing speed is maintained, but detection accuracy and reliability decrease
Solution Approach 1:
The detection system transitions from single-dimensional time-domain analysis to multi-dimensional analysis by incorporating frequency domain information through short-time Fourier transform. This adds a frequency dimension to the detection process, enabling more precise voice activity detection while maintaining processing speed through efficient spectral analysis
4Reliability
If multiple processing modules are cascaded, then processing comprehensiveness is improved, but system complexity and latency increase
Solution Approach 1:
The voice activity detection module acts as a preliminary gatekeeper before the main processing pipeline. By performing detection in advance and controlling the flow of subsequent processing based on detection results, the system achieves comprehensive processing when needed while avoiding unnecessary processing stages, thereby reducing overall system complexity and latency
Data Source
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AI summary
A speech signal processing method and a related device thereof are provided. The method may be applied to the audio field and includes: obtaining a user speech signal captured by a sensor; obtaining a corresponding vibration signal when a user generates a speech, where the vibration signal indicates a vibration feature of a body part of the user, and the body part is a part that vibrates correspondingly based on sound-making behavior when the user is making a sound; and obtaining target speech information based on the vibration signal and the user speech signal captured by the sensor. In this application, the vibration signal is used as a basis for speech recognition. Because the vibration signal does not include an external non-user speech mixed during complex acoustic transmission, and is slightly affected by other ambient noise (for example, affected by reverberation), this part of noise interference can be well suppressed, and good speech recognition effect can be achieved.