Error detection in automatic speech recognition

Seyed Farshid Hosseini Zavareh; Ingrid Zukerman; Su Nam Kim; Thomas Kleinbauer

Error detection in automatic speech recognition

Seyed Farshid Hosseini Zavareh, Ingrid Zukerman, Su Nam Kim, Thomas Kleinbauer

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

We offer a supervised machine learning approach for recognizing erroneous words in the output of a speech recognizer. We have investigated several sets of features combined with two word configurations, and compared the performance of two classifiers: Decision Trees and Naïve Bayes. Evaluation was performed on a corpus of 400 spoken referring expressions, with Decision Trees yielding a high recognition accuracy.

Original language	English
Title of host publication	Australasian Language Technology Association Workshop 2013 - Proceedings of the Workshop (ALTA)
Editors	Sarvnaz Karimi, Karin Verspoor
Place of Publication	Stroudsburg PA USA
Publisher	Association for Computational Linguistics (ACL)
Pages	101-105
Number of pages	5
Publication status	Published - 2013
Event	Australasian Language Technology Association Workshop 2013 - Queensland University of Technology, Brisbane, Australia Duration: 4 Dec 2013 → 6 Dec 2013 Conference number: 11th https://www.aclweb.org/anthology/events/alta-2013/ (Proceedings)

Conference

Conference	Australasian Language Technology Association Workshop 2013
Abbreviated title	ALTAW 2013
Country/Territory	Australia
City	Brisbane
Period	4/12/13 → 6/12/13
Internet address	https://www.aclweb.org/anthology/events/alta-2013/ (Proceedings)

Cite this

@inproceedings{5c09795baa4c4c559a7f2503148d71c3,

title = "Error detection in automatic speech recognition",

abstract = "We offer a supervised machine learning approach for recognizing erroneous words in the output of a speech recognizer. We have investigated several sets of features combined with two word configurations, and compared the performance of two classifiers: Decision Trees and Na{\"i}ve Bayes. Evaluation was performed on a corpus of 400 spoken referring expressions, with Decision Trees yielding a high recognition accuracy.",

author = "{Hosseini Zavareh}, {Seyed Farshid} and Ingrid Zukerman and Kim, {Su Nam} and Thomas Kleinbauer",

note = "Funding Information: This research was supported in part by grants DP110100500 and DP120100103 from the Australian Research Council. The authors thank Ma-sud Moshtaghi for his help with statistical issues. Publisher Copyright: {\textcopyright} Australasian Language Technology Association Workshop 2013, ALTA 2013 - Proceedings. All rights reserved.; Australasian Language Technology Association Workshop 2013, ALTAW 2013 ; Conference date: 04-12-2013 Through 06-12-2013",

year = "2013",

language = "English",

pages = "101--105",

editor = "Sarvnaz Karimi and Karin Verspoor",

booktitle = "Australasian Language Technology Association Workshop 2013 - Proceedings of the Workshop (ALTA)",

publisher = "Association for Computational Linguistics (ACL)",

url = "https://www.aclweb.org/anthology/events/alta-2013/",

}

Hosseini Zavareh, SF, Zukerman, I, Kim, SN & Kleinbauer, T 2013, Error detection in automatic speech recognition. in S Karimi & K Verspoor (eds), Australasian Language Technology Association Workshop 2013 - Proceedings of the Workshop (ALTA). Association for Computational Linguistics (ACL), Stroudsburg PA USA, pp. 101-105, Australasian Language Technology Association Workshop 2013, Brisbane, Queensland, Australia, 4/12/13.

Error detection in automatic speech recognition. / Hosseini Zavareh, Seyed Farshid; Zukerman, Ingrid; Kim, Su Nam et al.
Australasian Language Technology Association Workshop 2013 - Proceedings of the Workshop (ALTA). ed. / Sarvnaz Karimi; Karin Verspoor. Stroudsburg PA USA: Association for Computational Linguistics (ACL), 2013. p. 101-105.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Error detection in automatic speech recognition

AU - Hosseini Zavareh, Seyed Farshid

AU - Zukerman, Ingrid

AU - Kim, Su Nam

AU - Kleinbauer, Thomas

N1 - Conference code: 11th

PY - 2013

Y1 - 2013

N2 - We offer a supervised machine learning approach for recognizing erroneous words in the output of a speech recognizer. We have investigated several sets of features combined with two word configurations, and compared the performance of two classifiers: Decision Trees and Naïve Bayes. Evaluation was performed on a corpus of 400 spoken referring expressions, with Decision Trees yielding a high recognition accuracy.

AB - We offer a supervised machine learning approach for recognizing erroneous words in the output of a speech recognizer. We have investigated several sets of features combined with two word configurations, and compared the performance of two classifiers: Decision Trees and Naïve Bayes. Evaluation was performed on a corpus of 400 spoken referring expressions, with Decision Trees yielding a high recognition accuracy.

UR - http://www.scopus.com/inward/record.url?scp=84959159116&partnerID=8YFLogxK

M3 - Conference Paper

SP - 101

EP - 105

BT - Australasian Language Technology Association Workshop 2013 - Proceedings of the Workshop (ALTA)

A2 - Karimi, Sarvnaz

A2 - Verspoor, Karin

PB - Association for Computational Linguistics (ACL)

CY - Stroudsburg PA USA

T2 - Australasian Language Technology Association Workshop 2013

Y2 - 4 December 2013 through 6 December 2013

ER -

Error detection in automatic speech recognition

Abstract

Conference

Other files and links

Cite this