Continuous fundamental frequency prediction with deep neural networks
Bálint Tóth, Tamás Gábor Csapó · 2016
Deep learning is proven to outperform other machine learning methods in numerous research fields. However, previous approaches, like multispace probability distribution hidden Markov models still surpass deep learning methods in the prediction accuracy of speech fundamental frequency (F0), inter alia, due to its discontinuous behavior. The current research focuses on the application of feedforward deep neural networks (DNNs) for modeling continuous F0 extracted by a recent vocoding technique. In order to achieve lower validation error, hyperparameter optimization with manual grid search was carried out. The results of objective and subjective evaluations show that using continuous F0 trajectories, DNNs can reach the modeling performance of previous state-of-the-art solutions. The complexity of DNN architectures could be reduced in case of continuous F0 contours as well.