Abstract:
This paper presents a fast Gaussian computation algorithm based on subspace clustering of embedded speech recognition system to reduce the computation complexity. This algorithm does not need re-training model parameters. The experiments on embedded system show that the speed can be improved by above 20% and the recognition rate does not degrade much.
Key words:
Speech recognition,
Subspace clustering,
Gaussian computation,
Embedded system
摘要: 针对嵌入式语音识别系统,实现了基于子空间聚类的快速高斯计算,简化了HMM模型的计算复杂度,回避了声学模型重新训练的问题。在嵌入式系统上的实验数据表明,识别速度能获得20%以上的提高,而且识别率没有大幅降低。
关键词:
语音识别,
子空间聚类,
高斯计算,
嵌入式系统
LIU Bin; XIE Lingyun. Fast Gaussian Computation Based on Subspace Clustering[J]. Computer Engineering, 2007, 33(03): 35-36.
刘 斌;谢凌云. 基于子空间聚类的快速高斯计算[J]. 计算机工程, 2007, 33(03): 35-36.