CAJ | 학술논문

低资源条件下的语音关键词检测是一个具有挑战性的问题,因为传统的基于大词汇量连续语音识别(LVCSR)的语音关键词检测方法不再适用.针对此问题提出了一种基于深度神经网络(DNN)输出层后验概率特征和改进的动态时间规整(DTW)算法的语音关键词检测方法.采用无监督高斯混合模型(GMM)和中、英文 DNN 音素模型得出的输入特征构建互补的子系统,并在 SWS2013 多语种数据集上进行实验.结果表明:相对于基线系统,分数层面的多语种、多系统融合能够有效地提升语音关键词检测系统的性能.
저자원조건하적어음관건사검측시일개구유도전성적문제,인위전통적기우대사회량련속어음식별(LVCSR)적어음관건사검측방법불재괄용.침대차문제제출료일충기우심도신경망락(DNN)수출층후험개솔특정화개진적동태시간규정(DTW)산법적어음관건사검측방법.채용무감독고사혼합모형(GMM)화중、영문 DNN 음소모형득출적수입특정구건호보적자계통,병재 SWS2013 다어충수거집상진행실험.결과표명:상대우기선계통,분수층면적다어충、다계통융합능구유효지제승어음관건사검측계통적성능.
Spoken term detection in low-resource situations is a challenging task,because traditional large vocabu-lary continuous speech recognition(LVCSR)approaches are often unusable.We propose a query-by-example(QBE) spoken term detection(STD)method based on deep neural network(DNN)posteriorgram features and a modified dy-namic time warping(DTW)research approach.Subsystems are built with unsupervised Gaussian mixture model (GMM)and DNN monophone models trained on Chinese and English languages. The subsystems are then evaluated on the SWS2013 multilingual database of low-resource languages.The score-level fusion of these different languages and different subsystems is shown to improve performance significantly compared with the baseline results.