计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 169-173.doi: 10.11896/jsjkx.190800054
花明, 李冬冬, 王喆, 高大启
HUA Ming, LI Dong-dong, WANG Zhe, GAO Da-qi
摘要： 现有的说话人识别方法仍存在许多不足。基于话语级特征输入的端到端方法由于语音长短不一致需要将输入处理为同等大小,而特征训练加后验分类的两阶段方法使得识别系统过于复杂,这些因素都会影响模型的性能。文中提出了基于帧级特征的端到端说话人识别方法。模型采用帧级语音作为输入,同等大小的帧级特征有效解决了话语级语音输入长度不一致的问题,且帧级特征可保留更多的话者信息。与如今主流的两阶段法识别系统相比,端到端的识别方法将特征训练和分类打分一体化,简化了模型的复杂性。在训练阶段,每段语音被分帧成多个帧级语音输入到卷积神经网络(Convolutional Neural Networks,CNN)用于训练模型。在评估阶段,训练好的CNN模型对帧级语音进行分类,每段语音基于多个帧的预测得分计算该条语音数据的预测类别。每段语音的类别通过取各帧最多预测类别和各帧预测值平均的方法来计算。为了验证方法的有效性,使用普通话情感语音语料库(MASC)的语音数据进行训练和测试。实验结果表明,与现有方法相比,基于帧级特征的端到端识别方法的性能表现更佳。
中图分类号:
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