计算机系统应用
計算機繫統應用
계산궤계통응용
APPLICATIONS OF THE COMPUTER SYSTEMS
2015年
6期
143-147
,共5页
说话人识别%高斯混合模型%UBM%MCE%改进MCE
說話人識彆%高斯混閤模型%UBM%MCE%改進MCE
설화인식별%고사혼합모형%UBM%MCE%개진MCE
speaker recognition%Gaussian mixture model%UBM%MCE%improved MCE
针对实际问题中训练数据不足的特点,在对说话人建模时采用的是高斯混合模型—通用背景模型GMM-UBM,针对MCE训练算法中计算量大的显著问题,对其进行改进,改进的MCE算法不仅能使计算量减小,而且识别性能更佳。实验结果表明,在高斯混合数与说话人数不同的情况下,改进的MCE比传统MCE算法都要节省训练时间,且随着高斯混合数与说话人数的增长,节省的时间越多。针对采用MAP、MLLR、MAP\MLLR、EigenVoice方法作自适应得到的说话人模型,然后应用MCE算法与改进的MCE算法,改进的MCE算法比传统MCE方法识别率更高。
針對實際問題中訓練數據不足的特點,在對說話人建模時採用的是高斯混閤模型—通用揹景模型GMM-UBM,針對MCE訓練算法中計算量大的顯著問題,對其進行改進,改進的MCE算法不僅能使計算量減小,而且識彆性能更佳。實驗結果錶明,在高斯混閤數與說話人數不同的情況下,改進的MCE比傳統MCE算法都要節省訓練時間,且隨著高斯混閤數與說話人數的增長,節省的時間越多。針對採用MAP、MLLR、MAP\MLLR、EigenVoice方法作自適應得到的說話人模型,然後應用MCE算法與改進的MCE算法,改進的MCE算法比傳統MCE方法識彆率更高。
침대실제문제중훈련수거불족적특점,재대설화인건모시채용적시고사혼합모형—통용배경모형GMM-UBM,침대MCE훈련산법중계산량대적현저문제,대기진행개진,개진적MCE산법불부능사계산량감소,이차식별성능경가。실험결과표명,재고사혼합수여설화인수불동적정황하,개진적MCE비전통MCE산법도요절성훈련시간,차수착고사혼합수여설화인수적증장,절성적시간월다。침대채용MAP、MLLR、MAP\MLLR、EigenVoice방법작자괄응득도적설화인모형,연후응용MCE산법여개진적MCE산법,개진적MCE산법비전통MCE방법식별솔경고。
In practical problems, it adopts GMM - UBM as the background model when the training data is insufficient in speaker recognition system. Aiming at large amount of calculation in MCE training algorithm,it improved MCE. The improved MCE algorithm not only can reduce the amount of calculation, but also can get better recognition performance. The experimental results show that, under the different number of gaussian mixture and speakers, the improved MCE algorithm saves more training time than the traditional MCE algorithm, and as the growth of the number of gaussian mixture and speakers, the more time saving. In view of the MAP, MLLR, MAP\MLLR and EigenVoice adaptation ways which used in speaker recognition system modeling, then using MCE algorithm and the improved MCE algorithm, the improved MCE algorithm has higher recognition rate than the traditional MCE algorithm.
