{"title":"基于音高的语音切分重点检测","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":"{\"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}","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}
Pitch-based emphasis detection for segmenting speech recordings
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.