Skip to main content

LID-senone Extraction via Deep Neural Networks for End-to-End Language Identification

Ma, Jin, Song, Yan, McLoughlin, Ian Vince, Dai, Li-Rong, Ye, Zhong-Fu (2016) LID-senone Extraction via Deep Neural Networks for End-to-End Language Identification. In: Odyssey 2016: The Speaker and Language Recognition Workshop. . pp. 210-216. (KAR id:55055)

PDF Author's Accepted Manuscript
Language: English

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Download (722kB) Preview
Official URL


A key problem in spoken language identification (LID) is how to effectively model features from a given speech utterance. Recent techniques such as end-to-end schemes and deep neural networks (DNNs) utilising transfer learning such as bottleneck (BN) features, have demonstrated good overall performance, but have not addressed the extraction of LID-specific features.

We thus propose a novel end-to-end neural network which aims to obtain effective LID-senone representations, which we define as being analogous to senones in speech recognition. We show that LID-senones combine a compact representation of the original acoustic feature space with a powerful descriptive and discriminative capability. Furthermore, a novel incremental training method is proposed to extract the weak language information buried in the acoustic features of insufficient language resources. Results on the six most confused languages in NIST LRE 2009 show good performance compared to state-of-the-art BN-GMM/i-vector and BN-DNN/i-vector systems. The proposed end-to-end network, coupled with an incremental training method which mitigates against over-fitting, has potential not just for LID, but also for other resource constrained tasks.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: language identification; utterance representation; end-to-end neural network; LID-senone; incremental training method;
Subjects: T Technology
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 19 Apr 2016 10:58 UTC
Last Modified: 29 May 2019 17:14 UTC
Resource URI: (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince:
  • Depositors only (login required):


Downloads per month over past year