深层神经网络语音识别自适应方法研究

为了解决语音识别中深层神经网络的说话人与环境自适应问题，本文从语音信号中的说话人与环境因素的固有特点出发，提出了使用长时特征的自适应方案：首先基于高斯混合模型，建立说话人-环境联合补偿模型，对说话人与环境参数进行估计，将此参数作为长时特征；然后，将估计出来长时特征与短时特征一起送入深层神经网络，进行训练。Aurora4实验表明，这一方案可以有效地对说话人与环境因素进行分解，并提升自适应效果。

Adaptation Method for Deep Neural Network-based Speech Recognition

To handle the speaker and noise adaptation problem in deep neural network-based speech recognition system, this paper studied the inherent characters of speaker and noise random factors and proposed a new adaptation method using long term features. Firstly, a joint adaptation model was built based on Gaussian mixture models and the parameters of speaker and noise factors were estimated and used as long term features. Then, these long term features were used in deep neural network together with traditional short term features. Experiment results on Aurora4 database showed that this method could effectively factorize speaker and noise factors, and improve adaptation performance.