面向战场环境下的语种识别

Language Identification in Battlefield Environments

为实现语种识别在战场环境下保持较高的识别性能,提出一种基于语谱图灰度变换的语种识别方法。根据语音信息和战场环境下的噪声信息在语谱图上的分布特性,引入带通滤波;根据人耳听觉特性提取对数灰度语谱图;采用自动色阶算法抑制语谱图上的噪声信息,增强语种信息,并采用残差神经网络模型进行训练识别。实验结果表明:在-10 dB掠夺者战斗机驾驶舱噪声环境下,相对于线性灰度语谱图特征,识别正确率提升了46%;在其他噪声环境下,识别性能也大幅度提升。

To achieve accurate language identification in battlefield environments,a language identification method based on spectrogram gray transformation is proposed.Bandpass filtering is introduced based on the distribution characteristics of speech information and noise information in the spectrogram under battlefield noise conditions.Logarithmic gray spectrogram is extracted in line with human auditory characteristics.An automatic color adjustment algorithm is used to suppress noise information and enhance language information on the spectrogram,and a residual neural network model is used for training and identification.Experimental results show that compared with linear gray spectrogram features,the recognition accuracy is improved by 46%in the-10 dB Predator fighter cockpit noise environment.In other noise environments,the recognition performance is also greatly improved.