CAJ | 학술논문

声学模型自适应算法研究目的是缓解由测试数据和训练数据不匹配而引起的识别性能下降问题.基于深度神经网络(DNN)模型框架的自适应技术中,重训练是最直接的方法,但极容易出现过拟合现象,尤其是自适应数据稀疏的情况下.文章针对领域相关的自动语音识别任务,对典型的两种声学模型自适应算法进行了尝试,实验了基于线性变换网络的自适应方法和基于相对熵正则化准则的自适应方法,并对两种算法进行了详尽的系统性能比较.结果表明,在不同的自适应数据量下,相对熵正则化自适应方法均能表现出较好的性能.
성학모형자괄응산법연구목적시완해유측시수거화훈련수거불필배이인기적식별성능하강문제.기우심도신경망락(DNN)모형광가적자괄응기술중,중훈련시최직접적방법,단겁용역출현과의합현상,우기시자괄응수거희소적정황하.문장침대영역상관적자동어음식별임무,대전형적량충성학모형자괄응산법진행료상시,실험료기우선성변환망락적자괄응방법화기우상대적정칙화준칙적자괄응방법,병대량충산법진행료상진적계통성능비교.결과표명,재불동적자괄응수거량하,상대적정칙화자괄응방법균능표현출교호적성능.
Acoustic model adaptation algorithmaims at reducing the recognition performance degradation caused by the mismatch between training and testing data.Among the many adaptation techniques based on deep neural net-work(DNN),retraining is the most straightforward way.However it is prone to over-fitting,especially when adap-tation data is sparse.In this paper,two typical acoustic adaptation methods,namely linear transformation network adaptation and Kullback-Leibler divergence regularization adaptation,are experimentally explored for task-adaptation purpose.An elaborate comparison is made,and results show that KL divergence regularization technique achieves better performance under different amounts of adaptation data.