We investigate two systems for automatic disfluency detection on English and Mandarin conversational speech data . The first system combines various lexical and prosodic features in a Conditional Random Field model for detecting edit disfluencies . The second system combines acoustic and language model scores for detecting filled pauses through constrained speech recognition . We compare the contributions of different knowledge sources to detection performance between these two languages . Speech disfluencies are common phenomena in spontaneous speech . They consist of spoken words and phrases that represent self-correction , hesitation , and floor-grabbing behaviors , but do not add semantic information ; removing them yields the intended , fluent utterance . The presence of disfluencies in conversational speech data can cause problems for both downstream processing ( parsing and other natural language processing tasks ) and human readability of speech transcripts . There has been much research effort on automatic disfluency detection in recent years ( Shriberg and Stolcke , 1997 ; Snover et al . , 2004 ; Liu et al . , 2006 ; Lin and Lee , 2009 ; Schuler et al . , 2010 ; Georgila et al . , 2010 ; Zwarts and Johnson , 2011 ) , particularly from the DARPA EARS ( Effective , Affordable , Reusable Speech-to-Text ) MDE ( MetaData Extraction ) ( DARPA Information Processing Technology Office , 2003 ) program , which focused on the automatic transcription of sizable amounts of speech data and rendering such transcripts in readable form , for both conversational telephone speech ( CTS ) and broadcast news ( BN ) . However , the EARS MDE effort was focused on English only , and there has n't been much research on the effectiveness of similar automatic disfluency detection approaches for multiple languages . This paper presents three main innovations . First , we extend the EARS MDE-style disfluency detection approach combining lexical and prosodic features using a Conditional Random Field ( CRF ) model , which was employed for detecting disfluency on English conversational speech data ( Liu et al . , 2005 ) , to Mandarin conversational speech , as presented in Section 2 . Second , we implement an automatic filled pause detection approach through constrained speech recognition , as presented in Section 3 . Third , for both disfluency detection systems , we compare side-by-side contributions of different knowledge sources to detection performance for two languages , English and Mandarin , as presented in Section 4 . Conclusions appear in Section 5 . In conclusion , we have presented two automatic disfluency detection systems , one combining various lexical and prosodic features , and the other combining LVCSR acoustic and language model knowledge sources . We observed significant improvements in combining lexical and prosodic features over just employing word n-gram features , for both languages . When combining AM and LM knowledge sources for FP detection in constrained speech recognition , we found increasing LM weight improved both false alarm and miss rates for Mandarin but degraded the miss rate for English .

Challenge: Existing works on detecting speech disfluency which can be a problem for downstream processing and creating transcripts only focus on English.
Approach: They evaluate a Conditional Random Field-based edit disfluency detection model and a system which combines acoustic and language model that detects filled pauses in Mandarin.
Outcome: Their system comparisons in English and Mandarin show that combining lexical and prosodic features achieves improvements in both languages.