摘要
说话人分类系统的目的是将声音数据分段并按说话人进行分类。对每个说话人提取基于多距离麦克风的多时延特征,可以进一步提高说话人分类系统性能。但随着麦克风个数增加,多时延特征向量维数迅速增长。针对该问题,采用保留特征流形结构并降低计算代价的方法,提出一种基于多距离麦克风融合声学特征的多分量鉴别式保局投影算法,利用支持向量机分类器进行两说话人分类系统的训练和测试,实现会议场景下的说话人分类。实验结果证明,与传统DLPP等算法相比,该算法在大部分数据集上的分类性能较优,可将分类误差率降低至20%以下。
The purpose of the speaker classification system is to segment and classify speech data according to different speaker.It improves performance of the speaker classification system by extracting multi-delay feature based on multiple distance microphones.With the number of microphones increases,the multi-delay feature vector dimension grows rapidly.Aiming at this problem,a method is proposed with keeping manifold structure and reducing the computational cost.It uses the multi-component discriminant locality preserving projections algorithm based on multiple distance microphones acoustic merging feature.Experimental results show that Diarization Error Rate（DER） of this algorithm can be reduced to below 20% and is better than traditional methods in most of the data set.
出处
《计算机工程》
CAS
CSCD
2013年第8期1-4,共4页
Computer Engineering
基金
国家自然科学基金资助项目(61105017)
北京市自然科学基金资助项目(KZ201110005005)
关键词
说话人分类
多距离麦克风
多时延特征
声学融合特征
多分量鉴别式保局投影
分类误差率
speaker classification
multiple distance microphone
multi-delay feature
acoustic merging feature
multi-component discriminant locality preserving projection
Diarization Error Rate（DER）
