{"title":"实时新闻广播中无监督说话人变化检测的两阶段多特征集成方法","authors":"Lei Xie, Guangsen Wang","doi":"10.1109/CHINSL.2008.ECP.99","DOIUrl":null,"url":null,"abstract":"This paper presents a two-stage multi-feature integration approach for unsupervised speaker change detection in real-time news broadcasting. We integrate MFCC and LSP features (i.e. a perceptual feature plus a articulatory feature) in the metric-based potential speaker change detection stage to collect speaker boundary candidates as many as possible. We adopt a weighted Bayesian information criterion (BIC) to integrate boundary decisions from MFCC and LSP features in the speaker boundary confirmation stage. This multi-feature integration strategy makes use of the complementarity between perceptual features and articulatory features to achieve a performance gain. Speaker change detection experiments show that the multi- feature integration approach significantly outperforms the individual features with relative improvements of 26% over the LSP-only approach and 6% over the MFCC-only approach.","PeriodicalId":291958,"journal":{"name":"2008 6th International Symposium on Chinese Spoken Language Processing","volume":"133 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Two-Stage Multi-Feature Integration Approach to Unsupervised Speaker Change Detection in Real-Time News Broadcasting\",\"authors\":\"Lei Xie, Guangsen Wang\",\"doi\":\"10.1109/CHINSL.2008.ECP.99\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a two-stage multi-feature integration approach for unsupervised speaker change detection in real-time news broadcasting. We integrate MFCC and LSP features (i.e. a perceptual feature plus a articulatory feature) in the metric-based potential speaker change detection stage to collect speaker boundary candidates as many as possible. We adopt a weighted Bayesian information criterion (BIC) to integrate boundary decisions from MFCC and LSP features in the speaker boundary confirmation stage. This multi-feature integration strategy makes use of the complementarity between perceptual features and articulatory features to achieve a performance gain. Speaker change detection experiments show that the multi- feature integration approach significantly outperforms the individual features with relative improvements of 26% over the LSP-only approach and 6% over the MFCC-only approach.\",\"PeriodicalId\":291958,\"journal\":{\"name\":\"2008 6th International Symposium on Chinese Spoken Language Processing\",\"volume\":\"133 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 6th International Symposium on Chinese Spoken Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CHINSL.2008.ECP.99\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 6th International Symposium on Chinese Spoken Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CHINSL.2008.ECP.99","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Two-Stage Multi-Feature Integration Approach to Unsupervised Speaker Change Detection in Real-Time News Broadcasting
This paper presents a two-stage multi-feature integration approach for unsupervised speaker change detection in real-time news broadcasting. We integrate MFCC and LSP features (i.e. a perceptual feature plus a articulatory feature) in the metric-based potential speaker change detection stage to collect speaker boundary candidates as many as possible. We adopt a weighted Bayesian information criterion (BIC) to integrate boundary decisions from MFCC and LSP features in the speaker boundary confirmation stage. This multi-feature integration strategy makes use of the complementarity between perceptual features and articulatory features to achieve a performance gain. Speaker change detection experiments show that the multi- feature integration approach significantly outperforms the individual features with relative improvements of 26% over the LSP-only approach and 6% over the MFCC-only approach.