论文标题
在我之后重复：通过声音模仿的声学到发言映射的自我监督学习
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation
论文作者
论文摘要
我们提出了一种语音生产的计算模型，结合了预先训练的神经关节合成器，能够从有限的一组可解释的关节参数中重现复杂的语音刺激，这是一种基于DNN的内部前向模型，可预测基于反复网络恢复循环的循环命令的内部反向模型的内部反向模型的感官影响。从不同扬声器的原始声音数据数据，向前和反向模型都以自我监督的方式共同训练。仿真模拟客观和主观评估，并表现出令人鼓舞的表演。
We propose a computational model of speech production combining a pre-trained neural articulatory synthesizer able to reproduce complex speech stimuli from a limited set of interpretable articulatory parameters, a DNN-based internal forward model predicting the sensory consequences of articulatory commands, and an internal inverse model based on a recurrent neural network recovering articulatory commands from the acoustic speech input. Both forward and inverse models are jointly trained in a self-supervised way from raw acoustic-only speech data from different speakers. The imitation simulations are evaluated objectively and subjectively and display quite encouraging performances.
