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 .
Existing works on detecting speech disfluency which can be a problem for downstream processing and creating transcripts only focus on English.
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.
Their system comparisons in English and Mandarin show that combining lexical and prosodic features achieves improvements in both languages.