面向真实噪声环境的语种识别

Language Identification in Real Noisy Environments

语种识别受真实噪声环境的影响较大,识别效果不佳.为了解决真实噪声环境下语种识别的问题,提出一种基于对数灰度语谱图的图像处理语种识别方法.根据噪声能量和语音能量在语谱图上的分布规律对真实噪声中的语音信号进行带通滤波;再结合人耳听觉特性提取对数灰度语谱图;然后提取图像主成分特征作为语种特征,采用残差神经网络模型进行训练测试.实验结果表明,在掠夺者战斗机驾驶舱的环境下,所提方法的平均识别正确率相对于线性灰度语谱图方法提升了27.5%,在其他噪声环境下的平均识别正确率也有提升.

Language identification is heavily influenced by the real noise environment,resulting in poor identification results.To solve this problem,an image processing method for language identification is proposed based on the logarithmic gray-scale speech spectrogram.The logarithmic gray-scale speech spectrogram is obtained by combining the human auditory properties and the voice filtered in real noise environments according to the different distribution of noise and speech on the speech spectrogram.Then,the principal component of the spectrogram is extracted as language features and,a residual neural network model is used for training and testing.Experimental results show that the average identification rate of the proposed method is improved by 27.5%in the noisy cockpit of a Blackburn Buccaneer compared to the linear grey-scale speech spectrogram method.In other noisy environments,the average identification rate is also improved.