基于双向门控循环神经网络的声学-发音语音反演音频数据增强
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
AI总结：
本文提出基于双向门控循环神经网络的声学-发音语音反演系统，结合数据增强技术，在干净语音上较噪声鲁棒基线相关性相对提升5%，经说话人自适应后再提升6%。
摘要：
数据增强已被证明是通过增加训练数据的变异性来提升深度学习模型性能的一种有前景的方法。在先前开发噪声鲁棒声学-发音语音反演系统的工作中，我们已经证明了噪声增强对于提升含噪语音中语音反演性能的重要性。在本工作中，我们比较和对比了不同的数据增强方式，并展示了该技术不仅在含噪语音上，而且在干净语音数据上也能提升发音语音反演的性能。我们还提出了一种双向门控循环神经网络（Bidirectional Gated Recurrent Neural Network）作为语音反演系统，以替代先前使用的前馈神经网络。该反演系统使用梅尔频率倒谱系数（MFCCs）作为输入声学特征，并使用六个声道变量（TVs）作为输出发音特征。系统的性能通过在 U. Wisc. X射线微束数据库上计算估计的 TVs 与实际 TVs 之间的相关性来衡量。所提出的语音反演系统在干净语音数据上的相关性相对于基线噪声鲁棒系统显示出 5% 的相对改进。当预训练模型适应测试集中每个未见过的说话人时，平均相关性又提高了 6%。
英文摘要：
Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we have shown the importance of noise augmentation to improve the performance of speech inversion in noisy speech. In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory features. The Performance of the system was measured by computing the correlation between estimated and actual TVs on the U. Wisc. X-ray Microbeam database. The proposed speech inversion system shows a 5% relative improvement in correlation over the baseline noise robust system for clean speech data. The pre-trained model, when adapted to each unseen speaker in the test set, improves the average correlation by another 6%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
