计算机应用与软件2013，Vol.30Issue(5)：307-310,4.DOI:10.3969/j.issn.1000-386x.2013.05.087
多特征和神经网络相结合的语音端点检测算法
SPEECH ENDPOINTS DETECTION ALGORITHM BASED ON MULTIPLE FEATURES AND NEURAL NETWORK COMBINATION
金敏1
作者信息
- 1. 贵阳学院继续教育学院 贵州贵阳550003
- 折叠
摘要
Abstract
This paper presents a method for speech endpoint detection algorithm based on the combination of multiple features and neural network to improve the detection accuracy rate.Firstly,the features of short-time energy,time-domain variance and frequency-domain variance of speech signals are extracted respectively,and then these feature quantities are employed as the input of neural network for training and modelling,and finally the signal' s category are determined.Simulation experiments prove that compared with single feature speech endpoint detection algorithm,the proposed algorithm improves the detection accuracy rate,has better adaptability and robustness and has preferable detection ability on signals with different SNR.关键词
神经网络/语音端点/特征提取/信噪比Key words
Neural network / Speech endpoints / Feature extraction / Signal to noise ratio分类
信息技术与安全科学引用本文复制引用
金敏..多特征和神经网络相结合的语音端点检测算法[J].计算机应用与软件,2013,30(5):307-310,4.
