摘要
介绍一种适用于实时语音识别环境下的神经网络模型———动态识别神经网络(dynam ic recogn ition neu-ral network,DRNN).DRNN聚类学习的性能使得它非常适用于与在线学习方式相结合的实时语音识别系统.通过比较DRNN和隐含马尔科夫模型(h idden M arkov model,HMM),可以看到不论是在训练方面还是在识别方面,DRNN算法的计算复杂程度都要低于HMM算法.
A neural network model, dynamic recognition neural network （DRNN）, is presented for real-time speech recognition. The property of clustering learning of the DRNN makes it very suitable for real-time speech recognition with on-line learning ability. A comparison between the DRNN and hidden Markov model （ HMM ） shows that the computational complexity of the former is lower than that of the latter in both training and recognition.
出处
《应用科技》
CAS
2006年第6期18-20,23,共4页
Applied Science and Technology
关键词
语音识别
DRNN
自适应神经网络
聚类学习
HMM
speech recognition
dynamic recognition neural network （ DRNN ）
adaptive neural network
clustering learning
HMM
