The 2015 NIST Language Recognition Evaluation: The Shared View of I2R, Fantastic4 and SingaMS | Zendy

Kong Aik Lee | Zendy; Haizhou Li | Zendy; Li Deng | Zendy; Ville Hautamäki | Zendy; Wei Rao | Zendy; Xiong Xiao | Zendy; Anthony Larcher | Zendy; Hanwu Sun | Zendy; Trung Hieu Nguyen | Zendy; Guangsen Wang | Zendy; А. С. Сизов | Zendy; Jianshu Chen | Zendy; Ivan Kukanov | Zendy; Amir Hossein Poorjam | Zendy; Trung Ngô Trọng | Zendy; Chenglin Xu | Zendy; Haihua Xu | Zendy; Bin Ma | Zendy; Eng Siong Chng | Zendy; Sylvain Meignier | Zendy

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The 2015 NIST Language Recognition Evaluation: The Shared View of I2R, Fantastic4 and SingaMS

Author(s) -

Kong Aik Lee,

Haizhou Li,

Li Deng,

Ville Hautamäki,

Wei Rao,

Xiong Xiao,

Anthony Larcher,

Hanwu Sun,

Trung Hieu Nguyen,

Guangsen Wang,

А. С. Сизов,

Jianshu Chen,

Ivan Kukanov,

Amir Hossein Poorjam,

Trung Ngô Trọng,

Chenglin Xu,

Haihua Xu,

Bin Ma,

Eng Siong Chng,

Sylvain Meignier

Publication year - 2016

Publication title -

interspeech 2022

Language(s) - English

Resource type - Conference proceedings

DOI - 10.21437/interspeech.2016-624

Subject(s) - nist , computer science , bottleneck , artificial intelligence , speech recognition , artificial neural network , context (archaeology) , feature extraction , classifier (uml) , spoken language , natural language processing , paleontology , biology , embedded system

The series of language recognition evaluations (LRE's) conducted by the National Institute of Standards and Technology (NIST) have been one of the driving forces in advancing spoken language recognition technology. This paper presents a shared view of five institutions resulting from our collaboration toward LRE 2015 submissions under the names of I2R, Fan-tastic4, and SingaMS. Among others, LRE'15 emphasizes on language detection in the context of closely related languages, which is different from previous LRE's. From the perspective of language recognition system design, we have witnessed a major paradigm shift in adopting deep neural network (DNN) for both feature extraction and classifier. In particular, deep bottleneck features (DBF) have a significant advantage in replacing the shifted-delta-cepstral (SDC) which has been the only option in the past. We foresee deep learning is going to serve as a major driving force in advancing spoken language recognition system in the coming years.

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