Speaker Change Detection and Retrieval for Automatic Minute Taking
A book study on Speaker Change Detection and Spoken Sentence Retrieval for Automatic Minute Taking Applications.
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As speech recognition of broadcast news is becomingmore mature, research is moving into types of speechthat are more challenging. One such area isconversational speech. Initially telephoneconversations were studied but more recentlyattention moved to meeting recordings. Meetings aresocial events, were people exchange information.This book has presented a study on an automaticminute tacking (AMT) system. By the use of particularcharacteristic provided by SVM classification, thisbook successfully proposes the SVM trainingmisclassification rate (STMR) algorithm for theapplication of speaker chang...
As speech recognition of broadcast news is becoming
more mature, research is moving into types of speech
that are more challenging. One such area is
conversational speech. Initially telephone
conversations were studied but more recently
attention moved to meeting recordings. Meetings are
social events, were people exchange information.
This book has presented a study on an automatic
minute tacking (AMT) system. By the use of particular
characteristic provided by SVM classification, this
book successfully proposes the SVM training
misclassification rate (STMR) algorithm for the
application of speaker change detection (SCD).
Experimental results demonstrate that the STMR
performance of outperforms that of conventional
methods. Also, this book proposes two partial
matching algorithms and a keyword matching algorithm
that can be used to retrieve spoken sentences both on
training free and language independent.
more mature, research is moving into types of speech
that are more challenging. One such area is
conversational speech. Initially telephone
conversations were studied but more recently
attention moved to meeting recordings. Meetings are
social events, were people exchange information.
This book has presented a study on an automatic
minute tacking (AMT) system. By the use of particular
characteristic provided by SVM classification, this
book successfully proposes the SVM training
misclassification rate (STMR) algorithm for the
application of speaker change detection (SCD).
Experimental results demonstrate that the STMR
performance of outperforms that of conventional
methods. Also, this book proposes two partial
matching algorithms and a keyword matching algorithm
that can be used to retrieve spoken sentences both on
training free and language independent.
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