摘要
为了提高说话人识别的性能,提出一种基于GMM模型自适应说话人识别方法。该方法能自动根据不同的说话人选取不同时长的语音进行识别,从提取语音特征和计算识别概率两方面减少识别时间,在不降低识别率的前提下,比传统识别方法识别速度有大幅度提高。实验仿真表明,在保持正确识别率97%以上的情况下,总识别速度可提高4倍左右。该方法特别适合基于GMM的大集合说话人识别。
With the purpose of improving the performance of speaker recognition,an adaptive speaker recognition method based on GMM is proposed.It can automatically select different length of speech for different speakers so as to reduce the recognition time through two aspects： speaker acoustic features calculation and recognition probability estimation.So it can remarkably improve the recognition speed than customary methods while keeping the correct recognition ratio.Experiments show that the recognition speed is increased about 4 times while keeping the recognition ratio at the level of 97%.This novel method is very fit for large muster of speaker recognition based on GMM.
出处
《计算机与现代化》
2013年第7期91-93,共3页
Computer and Modernization
基金
江苏省自然科学基金资助项目(BK2009059)
解放军理工大学预研基金资助项目(2009TX08)
关键词
说话人识别
高斯混合模型
线性预测系数
自适应
speaker recognition
Gaussian mixture model（GMM）
linear prediction coefficient（LPC）
adaptation
