![](/images/graphics-bg.png)
Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features
المؤلفون المشاركون
Thatphithakkul, Nattanun
Suchato, Atiwong
Boonsuk, Sirinoot
Wutiwiwatchai, Chai
Punyabukkana, Proadpran
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-02-20
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-457357
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)