{"title":"Pitch-based emphasis detection for segmenting speech recordings","authors":"B. Arons","doi":"10.21437/ICSLP.1994-485","DOIUrl":null,"url":null,"abstract":"This paper describes a technique to automatically locate emphasized segments of a speech recording based on pitch. These salient portions can be used in a variety of applications, but were originally designed to be used in an interactive system that enables high-speed skimming and browsing of speech recordings. Previous techniques to detect emphasis have used Hidden Markov Models; emphasized regions in close temporal proximity were found to successfully create useful summaries of the recordings. The new research described herein presents a sim pler technique to detect salient segments and summarize a recording without using statistical models that require large amounts of training data. The algorithm adapts to the pitch range of a speaker, then automatically selects the regions of highest pitch activity as a measure of emphasis.","PeriodicalId":90685,"journal":{"name":"Proceedings : ICSLP. International Conference on Spoken Language Processing","volume":"140 1","pages":"1931-1934"},"PeriodicalIF":0.0000,"publicationDate":"1994-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"60","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings : ICSLP. International Conference on Spoken Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/ICSLP.1994-485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 60
Abstract
This paper describes a technique to automatically locate emphasized segments of a speech recording based on pitch. These salient portions can be used in a variety of applications, but were originally designed to be used in an interactive system that enables high-speed skimming and browsing of speech recordings. Previous techniques to detect emphasis have used Hidden Markov Models; emphasized regions in close temporal proximity were found to successfully create useful summaries of the recordings. The new research described herein presents a sim pler technique to detect salient segments and summarize a recording without using statistical models that require large amounts of training data. The algorithm adapts to the pitch range of a speaker, then automatically selects the regions of highest pitch activity as a measure of emphasis.