摘要
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,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。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,将估计出来的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,该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。
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, it built a joint adaptation model based on Gaussian mixture models and estimated and used the parame- ters of speaker and noise factors as long term features. Then, it used these long term features in deep neural network together with traditional short term features. Experiment results on Aurora4 database show that this method can effectively factorize speaker and noise factors, and improve adaptation performance.
出处
《计算机应用研究》
CSCD
北大核心
2016年第7期1966-1970,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61075020
61473168)
关键词
语音识别
声学模型自适应
深层神经网络
speech recognition
acoustic model adaptation
deep neural networks
