MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech (1909.07208v2)

Abstract: Clinical depression or Major Depressive Disorder (MDD) is a common and serious medical illness. In this paper, a deep recurrent neural network-based framework is presented to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, expanding training labels and transferred features are considered. The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accuracy of 76.27% and a root mean square error of 0.4 in assessing depression, while a root mean square error of 0.168 is achieved in predicting the depression severity levels. The proposed framework has several advantages (fastness, non-invasiveness, and non-intrusion), which makes it convenient for real-time applications. The performances of the proposed approach are evaluated under a multi-modal and a multi-features experiments. MFCC based high-level features hold relevant information related to depression. Yet, adding visual action units and different other acoustic features further boosts the classification results by 20% and 10% to reach an accuracy of 95.6% and 86%, respectively. Considering visual-facial modality needs to be carefully studied as it sparks patient privacy concerns while adding more acoustic features increases the computation time.

Automatic Clinical Depression Recognition from Speech Using MFCC-based RNN

The paper "MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech" presents a deep learning framework designed to detect and evaluate the severity of clinical depression through speech analysis. The authors utilize Mel Frequency Cepstral Coefficients (MFCC) as primary features in a recurrent neural network (RNN) to predict depression-related outcomes from audio recordings.

Summary of Methodology and Results

The proposed approach is built on the premise that speech can reveal significant indicators of depression, as depressed individuals often exhibit distinct vocal characteristics. The method extracts both low-level and high-level audio features from audio clips and uses these in a deep RNN framework to predict 24 scores from the Patient Health Questionnaire (PHQ-8) as well as a binary depression diagnosis.

Key to the proposed system is its ability to handle small datasets typical in this domain. The authors address this challenge through data augmentation and transfer learning, improving model robustness and reducing overfitting. Specifically, the paper trained their model on the DAIC-WOZ dataset, achieving an accuracy of 76.27% with a root mean square error (RMSE) of 0.4 in assessing depression, and an RMSE of 0.168 in predicting depression severity levels.

Noteworthy is the strategy of integrating additional features to enhance the model. When facial action units and other acoustic features were incorporated into the model, classification accuracy jumped to 95.6% and 86%, respectively, indicating the potential value of multimodal data fusion. However, this improvement should be balanced with concerns about increased computation time and privacy.

Implications and Future Directions

This research contributes significantly to the domain of affective computing and HCI-based healthcare by demonstrating the value of audio-based depression assessment tools. The framework's capability of providing fast, non-invasive, and non-intrusive diagnostics positions it as a viable candidate for real-time applications, potentially assisting clinicians in monitoring mental health more effectively.

From a theoretical standpoint, the paper underscores the pivotal role of MFCC features in speech-based depression recognition and suggests pathways for enhancing models with additional data modalities while considering computational and ethical implications.

Looking ahead, the paper provides a foundation for expanding speech recognition technologies in mental health diagnostics. The potential integration with gender-unbiased models or exploring entirely new feature sets could further refine accuracy and applicability. Additionally, a cross-comparison on datasets from various demographic backgrounds could offer more generalizable insights, implicitly driving progress within AI and machine learning applications in healthcare.

This work lays the groundwork for further exploration, pushing the frontier of how automatic speech analysis can serve mental health evaluation, potentially streamlining patient care by automating initial depression assessments.