论文标题
深层歧视性特征学习以识别口音
Deep Discriminative Feature Learning for Accent Recognition
论文作者
论文摘要
深度学习框架的口音识别与Deep Speaker识别类似，他们都期望将输入语音具有可识别的代表。 与说话者识别网络学到的个人级别功能相比，深刻的口音识别工作使一个更具挑战性的点，即为扬声器伪造群体级的重音功能。 在本文中，我们借用并改善了深层扬声器识别框架，以详细识别口音，我们采用卷积复发的神经网络作为前端编码器，并使用经常性的神经网络整合本地特征，以做出讲话级的重音表示。 在新颖的情况下，为了解决过度拟合，我们只需在训练过程中添加基于连接的时间分类的语音识别辅助任务，并且为了模棱两可的重音歧视，我们在面部识别中引入了一些强大的歧视性损失功能，以增强口音特征的歧视能力。 我们表明，我们提出的具有歧视性训练方法的网络（无数据仪器）明显领先于重音英语语音识别挑战2020年的重音分类轨道上的基线系统，在此，损失函数圈子损失已实现了对ACCENT代表的最佳歧视性优化。
Accent recognition with deep learning framework is a similar work to deep speaker identification, they're both expected to give the input speech an identifiable representation. Compared with the individual-level features learned by speaker identification network, the deep accent recognition work throws a more challenging point that forging group-level accent features for speakers. In this paper, we borrow and improve the deep speaker identification framework to recognize accents, in detail, we adopt Convolutional Recurrent Neural Network as front-end encoder and integrate local features using Recurrent Neural Network to make an utterance-level accent representation. Novelly, to address overfitting, we simply add Connectionist Temporal Classification based speech recognition auxiliary task during training, and for ambiguous accent discrimination, we introduce some powerful discriminative loss functions in face recognition works to enhance the discriminative power of accent features. We show that our proposed network with discriminative training method (without data-augment) is significantly ahead of the baseline system on the accent classification track in the Accented English Speech Recognition Challenge 2020, where the loss function Circle-Loss has achieved the best discriminative optimization for accent representation.
