基于DTW-GMM的光纤传感系统声纹识别方法

为了满足易燃易爆环境的声纹识别需求，设计了直线型萨格奈克干涉光纤声音传感系统，利用维纳滤波算法对语音数据进行了降噪，通过三电平削波法获取了基音周期特征，采用动态时间规整算法筛选了说话人样本，并提取了梅尔频率倒谱系数特征，运用高斯混合模型 期望最大化算法开展了声纹识别实验研究，同时探究了光纤声音传感系统的频率响应特性与声纹特征，研究了采集语音幅值对声纹识别结果的影响。实验结果表明，系统可实现300~3 500 Hz频率段的声音信号感知，声音幅值从0.9 V降至0.15 V时最大与次大对数似然值之差由35.5降至10.9，识别结果从成功变为失败。重复性实验表明，在10 km的传感光纤上，距声源2 m位置处，传感系统可对400段时长为3~5 s之间的文本无关语音段实现准确检测，且综合识别准确率为94.75％。本系统有望为易燃易爆环境中的设备故障、应急救援、渗漏监测等领域提供声纹识别的解决方案。

Voiceprint recognition method of optical fiber sensingsystem 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.