Voiceprint recognition method of optical fiber sensing system based on DTW-GMM
In order to meet the demand of voiceprint recognition in flammable and explosive environment.A linear Sagnac interference optical fiber acoustic sensor system has been designed.Speech data was denoised using the Wiener filtering algorithm,and pitch features were extracted through three-level clipping.Speaker samples were screened using dynamic time warping,and Mel-frequency cepstral coefficients were extracted as features.Voiceprint recognition experiments were conducted utilizing the Gaussian mixture model-expectation maximization algorithm,concurrently investigating the frequency response characteristics of the optical fiber acoustic sensor system and their relationship with voiceprint features.The influence of the amplitude of acquired speech on voiceprint recognition outcomes was studied.Experimental results demonstrate that the system can realize the sound signal perception in the frequency range of 300～3 500 Hz.When the sound amplitude decreases from 0.9 to 0.15 V,the difference between the maximum and second-largest log-likelihood values drops from 35.5 to 10.9,the recognition result changed from success to failure.Repetition experiments show that,at a distance of 2 meters from the sound source along a 10-kilometer sensing fiber,the system accurately recognizes 400 speech segments of 3 to 5 seconds duration,unrelated to any specific text,achieving an overall identification accuracy rate of 94.75％.This system holds promise as a solution for voiceprint recognition in applications such as equipment fault diagnosis and emergency response within flammable and explosive environments.
optical fiber sensingSagnac interferencevoiceprint recognitionGaussian mixture model
NETL
NSTL
万方数据
