SSCS '10Pub Date : 2010-10-29DOI: 10.1145/1878101.1878102
S. Scagliola
{"title":"Speech retrieval for interview data: technology push versus academic demand","authors":"S. Scagliola","doi":"10.1145/1878101.1878102","DOIUrl":"https://doi.org/10.1145/1878101.1878102","url":null,"abstract":".\u0000 In this contribution we outline the potential for spoken document retrieval from the perspective of professional scholarly users in the field of oral history.","PeriodicalId":123226,"journal":{"name":"SSCS '10","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127893021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SSCS '10Pub Date : 2010-10-29DOI: 10.1145/1878101.1878107
Dong Wang, Simon King, Joe Frankel, Ravichander Vipperla, N. Evans, Raphael Troncy
{"title":"Direct posterior confidence for out-of-vocabulary spoken term detection","authors":"Dong Wang, Simon King, Joe Frankel, Ravichander Vipperla, N. Evans, Raphael Troncy","doi":"10.1145/1878101.1878107","DOIUrl":"https://doi.org/10.1145/1878101.1878107","url":null,"abstract":"Spoken term detection (STD) is a fundamental task in spoken information retrieval. Compared to conventional speech transcription and keyword spotting, STD is an open-vocabul-ary task and is necessarily required to address out-of-vocabulary (OOV) terms. Approaches based on subword units, e.g. phonemes, are widely used to solve the OOV issue; however, performance on OOV terms is still significantly inferior to that for in-vocabulary (INV) terms.\u0000 The performance degradation on OOV terms can be attributed to a multitude of factors. A particular factor we address in this paper is that the acoustic and language models used for speech transcribing are highly vulnerable to OOV terms, which leads to unreliable confidence measures and error-prone detections.\u0000 A direct posterior confidence measure that is derived from discriminative models has been proposed for STD. In this paper, we utilize this technique to tackle the weakness of OOV terms in confidence estimation. Neither acoustic models nor language models being included in the computation, the new confidence avoids the weak modeling problem with OOV terms. Our experiments, set up on multi-party meeting speech which is highly spontaneous and conversational, demonstrate that the proposed technique improves STD performance on OOV terms significantly; when combined with conventional lattice-based confidence, a significant improvement in performance is obtained on both INVs and OOVs. Furthermore, the new confidence measure technique can be combined together with other advanced techniques for OOV treatment, such as stochastic pronunciation modeling and term-dependent confidence discrimination, which leads to an integrated solution for OOV STD with greatly improved performance.","PeriodicalId":123226,"journal":{"name":"SSCS '10","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124180721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SSCS '10Pub Date : 2010-10-29DOI: 10.1145/1878101.1878105
Charlotte Danesi, C. Clavel
{"title":"Impact of spontaneous speech features on business concept detection: a study of call-centre data.","authors":"Charlotte Danesi, C. Clavel","doi":"10.1145/1878101.1878105","DOIUrl":"https://doi.org/10.1145/1878101.1878105","url":null,"abstract":"This paper focuses on the detection of business concepts in call-centre conversation transcriptions. In the literature, information extraction behavior has been rarely deeply analyzed on such spontaneous speech data. We highlight here the various problems that are encountered when we attempt to extract information from such data. The recall and precision, which are obtained by comparing the concept detection method on automatic vs. manual transcription, are respectively at 74.8% and 77.7%. We find that, even though the concept detection is similar on the whole between manual and automatic transcriptions, spontaneous speech features tend to cause different behaviors of opinion-related concept detection on both transcriptions. On the one hand, spontaneous speech features, which frequently occur in these data, provokes silence (lack of detection) when detecting concepts on both transcriptions. On the other hand, ASR errors (e.g. due to homophony or disfluencies) tend to provoke noise (excessive detection) when detecting concept on automatic transcription.","PeriodicalId":123226,"journal":{"name":"SSCS '10","volume":"15 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126083982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}