Overview
Audio classifier detecting children's voices with 88% accuracy for child safety monitoring applications.
The Problem
Standard speech models perform poorly on children's higher-pitched voices and different speech patterns.
The Solution
Trained specialized classifier on children's speech datasets (ages 3-12) using MFCC features and CNN architecture.
Results & Impact
88%
Detection Accuracy
Child vs adult voice
3-12
Age Range
Years covered
Real-time
Processing Speed
On CPU
Key Impact
- Used in child safety monitoring applications
- Enabled parental control features
- Improved child-specific speech recognition
Technologies Used
PythonTensorFlowLibrosaAudio Signal ProcessingMFCC