Back to Search

RANSAC-based training data selection for speaker state recognition

Title	RANSAC-based training data selection for speaker state recognition
Author	Bozkurt, E., Erzin, E., Erdem, Ç. E., Erdem, Tanju
Publication Date:	2011
Publication Place	- The International Speech Communications Association
Subject	Speaker state challenge, Intoxication, Sleepiness, Ransac
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-61839-270-1
Record ID	5da6cd17-531b-4308-b689-ede0b95c98b3
Library Location	Computer Science
Date	2011
Notes	TÜBİTAK ; Türk Telekom
Sample Text	We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Subchallenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxicationubchallenge performance.

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

RANSAC-based training data selection for speaker state recognition

Author Bozkurt, E., Erzin, E., Erdem, Ç. E., Erdem, Tanju

Publication Date 2011

Publication Place - The International Speech Communications Association

Subject Speaker state challenge, Intoxication, Sleepiness, Ransac

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-61839-270-1

Record ID 5da6cd17-531b-4308-b689-ede0b95c98b3

Library Location Computer Science

Date 2011

Notes TÜBİTAK ; Türk Telekom

Sample Text We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Subchallenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxicationubchallenge performance.