Neutral to Lombard Speech Conversion with Deep Learning

In this paper, we propose several approaches for neutral to Lombard speech conversion. We study in particular the influence of different recurrent neural network architectures where their main hyper-parameters are carefully selected using a bandit-based approach. We also apply the Continuous Wavelet Transform (CWT) as a multi-resolution analysis framework to better model temporal dependencies of the different features selected. The speech conversion results obtained are validated by means of objective evaluations which highlight in particular the interest of the wavelet transform for the learning process.