Feb 24, 2022 · Prediction of Depression Severity Based on the Prosodic and Semantic Features With Bidirectional LSTM and Time Distributed CNN. Abstract ...

Mar 3, 2022 · They used a Bi-LSTM for both modalities and a Time-distributed Convolutional Neural Network (T-CNN) to enhance the temporal analysis of the data ...

Prediction of Depression Severity Based on the Prosodic and Semantic Features With Bidirectional LSTM and Time Distributed CNN · 20 Citations · 74 References.

In this paper, we propose a multimodality automated depression diagnosis system with prosodic and semantic features to predict the depression levels with the ...

In this article, we propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the ...

Feb 24, 2022 · In this proposed design, we have five time-distributed CNN blocks followed by a single-layer Bi-LSTM. The detailed architecture of each block is ...

Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN.

Prediction of Depression Severity Based on the Prosodic and Semantic Features With Bidirectional LSTM and Time Distributed CNN.

This article proposes a model to predict depression severity using prosodic and semantic features extracted from speech and text. The model uses a bidirectional ...

Prediction of depression severity based on the prosodic and semantic features with bidirectional LSTM and time distributed CNN. IEEE Trans Affect Comput. :1 ...