About: Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #69101 Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition
sameAs	Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition
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http://eprints.org/ontology/hasDocument	Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition (PDF) Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition (Other) Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition (Other) Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition (Other) Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition (Other) Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition (Other)
dc:hasVersion	Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition (PDF)
Title	Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2018-12-18
Creator	Yonghong Yan Shengyu Yao I. McLoughlin Xiaoxiao Miao
status	peer reviewed published
Publisher	IEEE
abstract	Recent research on generative adversarial nets (GAN) for language identification (LID) has shown promising results. In this paper, we further exploit the latent abilities of GAN networks to firstly combine them with deep neural network (DNN)-based i-vector approaches and then to improve the LID model using conditional generative adversarial net (cGAN) classification. First, phoneme dependent deep bottleneck features (DBF) combined with output posteriors of a pre-trained DNN for automatic speech recognition (ASR) are used to extract i-vectors in the normal way. These i-vectors are then classified using cGAN, and we show an effective method within the cGAN to optimize parameters by combining both language identification and verification signals as supervision. Results show firstly that cGAN methods can significantly outperform DBF DNN i-vector methods where 49-dimensional i-vectors are used, but not where 600-dimensional vectors are used. Secondly, training a cGAN discriminator network for direct classification has further benefit for low dimensional i-vectors as well as short utterances with high dimensional i-vectors. However, incorporating a dedicated discriminator network output layer for classification and optimizing both classification and verification loss brings benefits in all test cases.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/69101#authors
presented at	The 7th IEEE Workshop on Spoken Language Technology (SLT)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #69101 Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition

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