Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features

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 acoust...

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Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (1)
Main Authors: Boonsuk, Sirinoot, Suchato, Atiwong, Punyabukkana, Proadpran, Wutiwiwatchai, Chai, Thatphithakkul, Nattanun
Format: Article
Language:English
Subjects:
Acoustics
Behavior
Classification
Conditional random fields
Dynamical systems
Dynamics
Feature recognition
Gaussian
Language
Linguistics
Mathematical models
Probabilistic models
Sequences
Speech
Speech recognition
Statistical models
Studies
Systems analysis
Voice response technology
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/250160