  Accurately detecting voiced intervals in speech signals is a critical step in
pitch tracking and has numerous applications. While conventional signal
processing methods and deep learning algorithms have been proposed for this
task, their need to fine-tune threshold parameters for different datasets and
limited generalization restrict their utility in real-world applications. To
address these challenges, this study proposes a supervised voicing detection
model that leverages recorded laryngograph data. The model is based on a
densely-connected convolutional recurrent neural network (DC-CRN), and trained
on data with reference voicing decisions extracted from laryngograph data sets.
Pretraining is also investigated to improve the generalization ability of the
model. The proposed model produces robust voicing detection results,
outperforming other strong baseline methods, and generalizes well to unseen
datasets. The source code of the proposed model with pretraining is provided
along with the list of used laryngograph datasets to facilitate further
research in this area.
