| Presentation # | 3 |
| Session: | Detection, Paralinguistics and Coding |
| Location: | Kallirhoe Hall |
| Session Time: | Wednesday, December 19, 13:30 - 15:30 |
| Presentation Time: | Wednesday, December 19, 13:30 - 15:30 |
| Presentation: |
Poster
|
| Topic: |
Speaker/language recognition: |
| Paper Title: |
IMPROVED CONDITIONAL GENERATIVE ADVERSARIAL NET CLASSIFICATION FOR SPOKEN LANGUAGE RECOGNITION |
| Authors: |
Xiaoxiao Miao, The University of Kent / Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics / University of Chinese Academy of Sciences, China; Ian McLoughlin, The University of Kent, United Kingdom; Shengyu Yao, Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics / University of Chinese Academy of Sciences, China; Yonghong Yan, Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics / University of Chinese Academy of Sciences / Xinjiang Key Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, China |
| 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. |