Spoken language recognition with relevance feedback

Rong Tong; Haizhou Li; Ma Bin; Eng Siong Chng; Siu Yeung Cho

doi:10.1109/ICASSP.2007.367206

Spoken language recognition with relevance feedback

Rong Tong, Haizhou Li, Ma Bin, Eng Siong Chng, Siu Yeung Cho

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

1 Citation (Scopus)

Abstract

This paper applies relevance feedback technique in spoken language recognition task, in which we consider a test utterance as a test query. Assuming that we have a labeled multilingual corpus, we exploit the retrieved utterances from such a reference corpus to automatically augment the test query. Note that successful spoken language recognition relies on sufficient query data. The proposed method is especially effective for short query by expanding the query at a low cost. Experiments show that unsupervised relevance feedback reduces the relative equal-error-rate by 16.2%, 4.9% and 10.2% on NIST LRE 1996, 2003 and 2005 databases respectively for 3-second trials.

Original language	English
Title of host publication	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Pages	IV861-IV864
DOIs	https://doi.org/10.1109/ICASSP.2007.367206
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States Duration: 15 Apr 2007 → 20 Apr 2007

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	4
ISSN (Print)	1520-6149

Conference

Conference	2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/Territory	United States
City	Honolulu, HI
Period	15/04/07 → 20/04/07

Keywords

Relevance feedback
Spoken language recognition
Vector space model

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2007.367206

Cite this

Tong, R., Li, H., Bin, M., Chng, E. S., & Cho, S. Y. (2007). Spoken language recognition with relevance feedback. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 (pp. IV861-IV864). Article 4218237 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 4). https://doi.org/10.1109/ICASSP.2007.367206

@inproceedings{796874c2ca634caeb342dc80bc2aa812,

title = "Spoken language recognition with relevance feedback",

abstract = "This paper applies relevance feedback technique in spoken language recognition task, in which we consider a test utterance as a test query. Assuming that we have a labeled multilingual corpus, we exploit the retrieved utterances from such a reference corpus to automatically augment the test query. Note that successful spoken language recognition relies on sufficient query data. The proposed method is especially effective for short query by expanding the query at a low cost. Experiments show that unsupervised relevance feedback reduces the relative equal-error-rate by 16.2%, 4.9% and 10.2% on NIST LRE 1996, 2003 and 2005 databases respectively for 3-second trials.",

keywords = "Relevance feedback, Spoken language recognition, Vector space model",

author = "Rong Tong and Haizhou Li and Ma Bin and Chng, {Eng Siong} and Cho, {Siu Yeung}",

year = "2007",

doi = "10.1109/ICASSP.2007.367206",

language = "English",

isbn = "1424407281",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "IV861--IV864",

booktitle = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07",

note = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 ; Conference date: 15-04-2007 Through 20-04-2007",

}

Tong, R, Li, H, Bin, M, Chng, ES & Cho, SY 2007, Spoken language recognition with relevance feedback. in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07., 4218237, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 4, pp. IV861-IV864, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI, United States, 15/04/07. https://doi.org/10.1109/ICASSP.2007.367206

Spoken language recognition with relevance feedback. / Tong, Rong; Li, Haizhou; Bin, Ma et al.
2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07. 2007. p. IV861-IV864 4218237 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 4).

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

TY - GEN

T1 - Spoken language recognition with relevance feedback

AU - Tong, Rong

AU - Li, Haizhou

AU - Bin, Ma

AU - Chng, Eng Siong

AU - Cho, Siu Yeung

PY - 2007

Y1 - 2007

N2 - This paper applies relevance feedback technique in spoken language recognition task, in which we consider a test utterance as a test query. Assuming that we have a labeled multilingual corpus, we exploit the retrieved utterances from such a reference corpus to automatically augment the test query. Note that successful spoken language recognition relies on sufficient query data. The proposed method is especially effective for short query by expanding the query at a low cost. Experiments show that unsupervised relevance feedback reduces the relative equal-error-rate by 16.2%, 4.9% and 10.2% on NIST LRE 1996, 2003 and 2005 databases respectively for 3-second trials.

AB - This paper applies relevance feedback technique in spoken language recognition task, in which we consider a test utterance as a test query. Assuming that we have a labeled multilingual corpus, we exploit the retrieved utterances from such a reference corpus to automatically augment the test query. Note that successful spoken language recognition relies on sufficient query data. The proposed method is especially effective for short query by expanding the query at a low cost. Experiments show that unsupervised relevance feedback reduces the relative equal-error-rate by 16.2%, 4.9% and 10.2% on NIST LRE 1996, 2003 and 2005 databases respectively for 3-second trials.

KW - Relevance feedback

KW - Spoken language recognition

KW - Vector space model

UR - http://www.scopus.com/inward/record.url?scp=34547549144&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2007.367206

DO - 10.1109/ICASSP.2007.367206

M3 - Conference contribution

AN - SCOPUS:34547549144

SN - 1424407281

SN - 9781424407286

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - IV861-IV864

BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07

T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07

Y2 - 15 April 2007 through 20 April 2007

ER -

Spoken language recognition with relevance feedback

Abstract

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Keywords

ASJC Scopus subject areas

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