Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features
Joint Authors
Thatphithakkul, Nattanun
Suchato, Atiwong
Boonsuk, Sirinoot
Wutiwiwatchai, Chai
Punyabukkana, Proadpran
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-20
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Spoken language recognition (SLR) has been of increasing interest in multilingual speech recognition for identifying the languages of speech utterances.
Most existing SLR approaches apply statistical modeling techniques with acoustic and phonotactic features.
Among the popular approaches, the acoustic approach has become of greater interest than others because it does not require any prior language-specific knowledge.
Previous research on the acoustic approach has shown less interest in applying linguistic knowledge; it was only used as supplementary features, while the current state-of-the-art system assumes independency among features.
This paper proposes an SLR system based on the latent-dynamic conditional random field (LDCRF) model using phonological features (PFs).
We use PFs to represent acoustic characteristics and linguistic knowledge.
The LDCRF model was employed to capture the dynamics of the PFs sequences for language classification.
Baseline systems were conducted to evaluate the features and methods including Gaussian mixture model (GMM) based systems using PFs, GMM using cepstral features, and the CRF model using PFs.
Evaluated on the NIST LRE 2007 corpus, the proposed method showed an improvement over the baseline systems.
Additionally, it showed comparable result with the acoustic system based on i-vector.
This research demonstrates that utilizing PFs can enhance the performance.
American Psychological Association (APA)
Boonsuk, Sirinoot& Suchato, Atiwong& Punyabukkana, Proadpran& Wutiwiwatchai, Chai& Thatphithakkul, Nattanun. 2014. Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-457357
Modern Language Association (MLA)
Boonsuk, Sirinoot…[et al.]. Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features. Mathematical Problems in Engineering No. 2014 (2014), pp.1-16.
https://search.emarefa.net/detail/BIM-457357
American Medical Association (AMA)
Boonsuk, Sirinoot& Suchato, Atiwong& Punyabukkana, Proadpran& Wutiwiwatchai, Chai& Thatphithakkul, Nattanun. Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-457357
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-457357