数据采集与处理2016，Vol.31Issue(2)：407-414,8.DOI:10.16337/j.1004-9037.2016.02.023
基于声学分段模型的无监督语音样例检测
Unsupervised Query-by-Example Spoken Term Detection Based on Acoustic Segment Models
摘要
Abstract
A study of acoustic segment models (ASM s) for unsupervised query‐by‐example spoken term detec‐tion is presented .Firsty ,a Gaussian mixture model(GMM) is trained without any transcription information to label speech frames with Gaussian posteriorgram .Hierarchical agglomerative clustering is used to decompose the posterior features into acoustically exhibiting segments .A label is assigned to each result segment by k‐means clustering ,then posteriorgram is faciltitated to train ASMs .In query matching phase ,Viterbi decode is proposed to represent query and test posteriorgrams as ASM sequences .Dynamic match lattice spotting based on minimum edit distance is used to locate possible occurrences of the query term .Experimental results show that the proposed method outperforms traditional GMM and ASMs tokenizers .关键词
声学分段模型/语音样例检测/后验概率特征/无监督Key words
acoustic segment models/query-by-example spoken term detection/posterior features/unsu-pervised分类
信息技术与安全科学引用本文复制引用
李勃昊,张连海,郑永军..基于声学分段模型的无监督语音样例检测[J].数据采集与处理,2016,31(2):407-414,8.基金项目
国家自然科学基金（61175017）资助项目。 （）
