Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision
This work addresses stress detection for mental health applications using speech data, but it is incremental as it builds on existing LSTM and attention methods with a novel data augmentation approach.
The paper tackled psychological stress detection from spoken language by proposing an attention-based LSTM model, achieving 74.1% accuracy and 74.3% f-score, with distant supervision fine-tuning improving performance by 1.6% accuracy and 2.1% f-score.
We propose a Long Short-Term Memory (LSTM) with attention mechanism to classify psychological stress from self-conducted interview transcriptions. We apply distant supervision by automatically labeling tweets based on their hashtag content, which complements and expands the size of our corpus. This additional data is used to initialize the model parameters, and which it is fine-tuned using the interview data. This improves the model's robustness, especially by expanding the vocabulary size. The bidirectional LSTM model with attention is found to be the best model in terms of accuracy (74.1%) and f-score (74.3%). Furthermore, we show that distant supervision fine-tuning enhances the model's performance by 1.6% accuracy and 2.1% f-score. The attention mechanism helps the model to select informative words.