Voiceprint recognition algorithm box background
Voiceprint recognition is a biometric technology that uses the analysis of an individual?s vocal features to verify or confirm their identity. Voiceprint recognition utilizes an individual?s voice, including speech, pronunciation habits, pitch, pace, and other information, for recognition.
Voiceprint recognition technology has a wide range of applications, including but not limited to security, personal identity verification, telephone banking services, and evidence analysis in the judicial field. Its advantage lies in not being affected by changes in appearance, and compared to other biometric technologies such as fingerprint or iris recognition, voiceprint recognition is also easier to perform remotely or secretly. However, voiceprint recognition also faces some challenges, such as environmental noise, changes in the speaker?s emotions or health status, and other factors that may affect the accuracy of recognition.
With the advancement of technology and the increasing number of application scenarios, voiceprint recognition technology is gradually becoming one of the important branches in the field of biometric recognition, providing more secure and convenient identity verification solutions for various fields.
Technical Parameter
Voiceprint recognition model based on Pytorch: The model is a deep learning based speaker recognition system that incorporates channel attention mechanism, information propagation, and aggregation operations into its structure. The key components of this model include multiple frame level TDNN layers, a statistical pooling layer, and two sentence level fully connected layers. In addition, it is equipped with a softmax layer and a loss function of cross entropy.
Feature Extraction: Pre emphasis ->Split addition window ->Discrete Fourier Transform ->Mel filter bank ->Inverse Discrete Fourier Transform ->Image
Model training set:>10000 training samples
Sound types: Sound types are mainly divided into five categories: domestic noise, construction noise, industrial noise, traffic noise, and natural noise, including no less than 50 subcategories such as thunder, wind, knocking, insect and bird chirping
Voiceprint recognition accuracy: ≥ 85%
Recognition response rate:>3s
Calling method: Supports cloud calling or local terminal calling
Technical agreement: Supports HTTP protocol
Voiceprint library classification
First level classification: five categories: natural noise, domestic noise, construction noise, industrial noise, and traffic noise. Classification criteria: HJ640 standard, noise pollution prevention and control report, noise environmental impact assessment, noise law, etc;
Secondary classification: distinguished by application scenarios or common characteristics of sound;
Third level classification: As a result of sub station recognition, the original sound types are merged and optimized within the same category.
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