Deep bottleneck network based i-vector representation for language identification

By Yan Song, Xinhai Hong, Bing Jiang, Ruilian Cui, Ian Vince McLoughlin, Lirong Dai. 2015

33 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Weicheng Cai
2. Jinkun Chen
3. Ming Li
4. Ian Vince McLoughlin
5. Wang Geng
6. Yan Song
7. Lirong Dai
8. Ma Jin

In Signal Processing

1. Weicheng Cai
2. Jinkun Chen
3. Ming Li
4. Ian Vince McLoughlin
5. Yan Song
6. Lirong Dai
7. Ma Jin
8. Bo Xu

Citations in the corpus

• Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System (2018, background): “…perceptual linear prediction (PLP), shifted delta coeffi-cients (SDC) features [2, 5], and automatically learned phoneme discriminant features from deep neural networks (DNN), such as bottleneck features [6, 7, 8], phoneme posterior probability (PPP) features [9], and tandem features [10, 11].”
• End-to-End Language Identification Using Attention-Based Recurrent Neural Networks (2016, methodology): “Moreover, a unified DBN which covers both the front-end high-level feature extraction and back-end acoustic modeling stage is proposed to apply neural networks to LID task at larger scale [11].”
• LID-Senones and Their Statistics for Language Identification (2018, methodology): “The baseline LID system used for comparison in this paper is the DNN/i-vector method [5], [9], [10] using statistics from bottleneck [7] features to form an i-vector [6], [11].”