Unsupervised Query-by-example spoken term detection based on DPHMM tokenizer

摘要：

　　This paper investigates the use of Dirichlet process hidden Markov model(DPHMM)tokenizer for the template matching based query-by-example spoken term detection(QbE-STD)task.DPHMM can be obtained following an unsupervised iterative procedure without any training transcriptions.The STD performance of the DPHMM tokenizer is evaluated on TIMIT Corpus.We construct three kinds of DPHMM based QbE-STD systems.Compared to GMM baseline system,posteriorgram based system has a better detection precision,acoustic unit has a better detection speed,and pseudo feedback based system has a good balance between precision and speed.Although nonparametric DPHMM is still worse than supervised language-matched phoneme recognizer,but it outperforms other unsupervised tokenizers and language-mismatched phoneme recognizer.

关键词： unsupervised Dirichlet process hidden Markov model query-by-example spoken term detection pseudo feedback

作者: Cao Jiankai Zhang Lianhai

作者单位: NDSC Zhengzhou,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 1321-1325

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）