GAN-Based Domain Adaptation for Gender Identification
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摘要: 在实际应用场景中，由于实际语音数据与模型训练数据存在较大差异，导致基于音频的性别识别模型的性能严重下降。为了解决这一问题，本文提出了一种结合GAN和GhostVLAD层的域自适应模型。基于GhostVLAD的引入可有效减少语音中噪声和无关信息的干扰，而基于GAN思想的训练方法可以实现模型对目标域数据的自适应。在对抗训练中，通过引入辅助损失保持网络对性别特征的表征能力。采用Voxceleb1数据集作为源域，Audioset和Movie数据集分别作为目标域，对本文的域自适应模型的性能进行测试实验。实验结果表明，相比于基于卷积神经网络的性别识别模型，本文模型可将性别识别的准确率分别提高5.13%和7.72%。Abstract: Gender identification is a relevant task in speaker verification. It can also be used as an auxiliary tool in Automatic Speech Recognition(ASR) to improve model performance. To improve the accuracy of gender identification, some researchers have proposed some scheme based on deep learning. However, compared to the acoustic conditioned data in training, speech data in application scenarios is usually masked by the background noise, such as music, environmental noise, or background chatter, etc. Thus the performance of gender identification model is seriously degraded due to the data difference between training and actual use. In order to solve this problem, we propose a domain adaptive model combining Generative Adversarial Network(GAN) and GhostVLAD layer. The introduction of GhostVLAD can effectively reduce the interference of noise and irrelevant information in speech. Besides, the domain adaptation with GANs can realize the adaptation of the model to the target domain data. Furthermore, to maintain the network’s ability of gender discrimination, we use an auxiliary loss during the adversarial training. Voxceleb1 is chosen as the source domain data, while Audioset and Movie as the target domain data. The experimental results show that the proposed model achieves a relative improvement of 5.13% and 7.72% over the baseline in target domain data.
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表 1 CNN-GV模型的结构
Table 1. Structure of the CNN-GV model
Input size $\left( {{\rm{96}} \times {\rm{64}} \times {\rm{1}}} \right)$ 
Output size Conv2d, ${\rm{3}} \times {\rm{3}}$ ,32, stride(1,1)
${\rm{96}} \times {\rm{64}} \times {\rm{32}}$ 
Conv2d, ${\rm{3}} \times {\rm{3}}$ ,32, stride(1,1)
${\rm{96}} \times {\rm{64}} \times {\rm{32}}$ 
Conv2d, ${\rm{3}} \times {\rm{3}}$ ,64, stride(2,2)
${\rm{48}} \times {\rm{32}} \times {\rm{64}}$ 
Conv2d, ${\rm{3}} \times {\rm{3}}$ ,64, stride(1,1)
${\rm{48}} \times {\rm{32}} \times {\rm{64}}$ 
Max pool, ${\rm{1}} \times {\rm{2}}$ ,stride(1,2)
${\rm{48}} \times {\rm{16}} \times {\rm{64}}$ 
Conv2d, ${\rm{3}} \times {\rm{3}}$ ,128, stride(2,2)
${\rm{24}} \times {\rm{8}} \times {\rm{128}}$ 
Conv2d, ${\rm{3}} \times {\rm{3}}$ ,128, stride(1,1)
${\rm{24}} \times {\rm{8}} \times {\rm{128}}$ 
Max pool, ${\rm{2}} \times {\rm{2}}$ ,stride(2,2)
${\rm{12}} \times {\rm{4}} \times {\rm{128}}$ 
Conv2d, ${\rm{3}} \times {\rm{3}}$ ,256, stride(2,2)
${\rm{6}} \times {\rm{2}} \times {\rm{256}}$ 
Ghost VLAD 5120 Dense,1024 1024 Dense,256 256 Softmax,2 2 
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