计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
15期
206-210
,共5页
朱俊梅%顾明亮%张世形%贾晶晶
硃俊梅%顧明亮%張世形%賈晶晶
주준매%고명량%장세형%가정정
性别识别%改进Citation-K最近邻(KNN)算法%高斯混合模型%改进Hausdorff距离
性彆識彆%改進Citation-K最近鄰(KNN)算法%高斯混閤模型%改進Hausdorff距離
성별식별%개진Citation-K최근린(KNN)산법%고사혼합모형%개진Hausdorff거리
gender identification%modified Citation-K-Nearest Neighbor(KNN)algorithm%Gaussian Mixed Model(GMM)%modified Hausdorff distance
为了简化系统模型训练方法，提高性别识别系统的整体效率，提出了一种基于改进Citation-KNN算法的说话人性别识别方法。该方法将连续语音切分，训练每段语音的高斯混合模型（Gaussian Mixture Model，GMM）作为多示例包，其所有混合元为相应包中示例；采用改进的Hausdorff距离作为包与包之间的距离测度，通过Citation-KNN算法进行性别识别。该方法以多示例包间距离为分类依据，简化了系统训练，且识别率优于一些传统算法。
為瞭簡化繫統模型訓練方法，提高性彆識彆繫統的整體效率，提齣瞭一種基于改進Citation-KNN算法的說話人性彆識彆方法。該方法將連續語音切分，訓練每段語音的高斯混閤模型（Gaussian Mixture Model，GMM）作為多示例包，其所有混閤元為相應包中示例；採用改進的Hausdorff距離作為包與包之間的距離測度，通過Citation-KNN算法進行性彆識彆。該方法以多示例包間距離為分類依據，簡化瞭繫統訓練，且識彆率優于一些傳統算法。
위료간화계통모형훈련방법，제고성별식별계통적정체효솔，제출료일충기우개진Citation-KNN산법적설화인성별식별방법。해방법장련속어음절분，훈련매단어음적고사혼합모형（Gaussian Mixture Model，GMM）작위다시례포，기소유혼합원위상응포중시례；채용개진적Hausdorff거리작위포여포지간적거리측도，통과Citation-KNN산법진행성별식별。해방법이다시례포간거리위분류의거，간화료계통훈련，차식별솔우우일사전통산법。
To simplify the method of model training and improve efficiency of gender identification system, an algorithm of speaker gender identification based on modified Citation-KNN algorithm and a new speech Multi-Instance(MI)bag generating method are introduced. Continuous speech is segmented and sub-paragraph speech is modeled with a Gaussian Mixed Model(GMM). The generated GMM is treated as an MI bag and the components of the GMM are the instances of the corresponding bag. Modified Hausdorff distance is used to measure the distance between two bags. Experimental results show that this method can identify speakers’gender effectively and is superior to traditional algorithms in the correct recognition rate.
