Language recognition system using language branch discriminative information

Author

Xianliang Wang ; Yulong Wan ; Lin Yang ; Ruohua Zhou ; Yonghong Yan

Author_Institution

Key Lab. of Speech Acoust. & Content Understanding, Inst. of Acoust., Beijing, China

fYear

2014

fDate

4-9 May 2014

Firstpage

5327

Lastpage

5331

Abstract

This paper presents our study of using language branch discriminative information effectively for language recognition. Language branch variability (LBV) method based on factor analysis techniques is proposed. In LBV method, language branch variability factor is obtained by concatenating low-dimensional factors in the language branch variability spaces. Language models are trained within language branches and between languages. Experiments on NIST 2011 Language Recognition Evaluation (LRE) 30s, 10s and 03s tasks show the proposed LBV method provides stable improvement compared to the state-of-art total variability (TV) approach. In 30-second task, it gains relative improvement by 14.6% in equal error rate (EER) and 12.9% in minimum decision cost value (minDCF), and in new metrics of NIST 2011 LRE, it leads to relative improvement of 7.2%-17.7%.

Keywords

error analysis; speech recognition; EER; LBV; NIST 2011 language recognition evaluation; equal error rate; factor analysis; language branch discriminative information; language branch variability; language branches; language recognition system; low-dimensional factor concatenation; minDCF; minimum decision cost value; Acoustics; Measurement; NIST; Speech; Speech recognition; Support vector machines; TV; discriminative information; factor analysis; language branch variability; language recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on

Conference_Location

Florence

Type

conf

DOI

10.1109/ICASSP.2014.6854620

Filename

6854620