Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions

11/21/2018 ∙ by Albert Haque, et al. ∙ 8

With more than 300 million people depressed worldwide, depression is a global problem. Due to access barriers such as social stigma, cost, and treatment availability, 60 services. Effective and efficient diagnosis relies on detecting clinical symptoms of depression. Automatic detection of depressive symptoms would potentially improve diagnostic accuracy and availability, leading to faster intervention. In this work, we present a machine learning method for measuring the severity of depressive symptoms. Our multi-modal method uses 3D facial expressions and spoken language, commonly available from modern cell phones. It demonstrates an average error of 3.67 points (15.3 clinically-validated Patient Health Questionnaire (PHQ) scale. For detecting major depressive disorder, our model demonstrates 83.3 specificity. Overall, this paper shows how speech recognition, computer vision, and natural language processing can be combined to assist mental health patients and practitioners. This technology could be deployed to cell phones worldwide and facilitate low-cost universal access to mental health care.

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1 Introduction

Worldwide, more than 300 million people are depressed who2018depression . In the worst case, depression can lead to suicide, with close to 800,000 people committing suicide every year. In general, patients with mental disorders are seen by a wide spectrum of health care providers, including primary care physicians kroenke2001phq . However, compared to physical illnesses, mental disorders are more difficult to detect. The burden of mental health is exacerbated by barriers to care such as social stigma, financial cost, and a lack of accessible treatment options. To address entrenched barriers to care, scalable approaches for detecting mental health symptoms have been called for kazdin2011rebooting . If successful, early detection may impact access for the 60% of mentally-ill adults who do not receive treatment nami .

In practice, clinicians identify depression in patients by first measuring the severity of depressive symptoms111Depressive symptoms include feelings of worthlessness, loss of interest in hobbies, or thoughts of suicide. during in-person clinical interviews. During these interviews, clinicians assess both verbal and non-verbal indicators of depressive symptoms including monotone pitch, reduced articulation rate, lower speaking volumes hall1995nonverbal ; sobin1997psychomotor , fewer gestures, and more downward gazes waxer1974nonverbal ; schelde1998major ; perez2003nonverbal . If such symptoms persist for two weeks american2013diagnostic , the patient is considered to have a major depressive episode. Structured questionnaires have been developed and validated in clinical populations to assess the severity of depressive symptoms. One of the most common questionnaires is the Patient Health Questionnaire (PHQ) kroenke2009phq . This clinically-validated tool measures depression symptom severity across several personal dimensions kroenke2002phq . Assessing symptom severity is time-intensive, and critical for both initial diagnosis and improvement across time. Thus, AI-based solutions to assessing symptom severity may address entrenched barriers to access and treatment.

We envision an AI-based solution where depressed individuals can receive evidence-based mental health services while avoiding existing barriers to access. Such a solution could leverage multi-modal sensors or text messages, as is common on modern smartphones, to increase timely and cost-effective symptom screening althoff2016large . Conversational AIs are another potential solution miner2017talking ; miner2016smartphone . Our hope is that automated feedback will (i) provide actionable feedback to individuals who may be depressed, and (ii) improve automated depression screening tools for clinicians, by including visual, audio, and linguistic signals.

Contributions. We propose a machine learning method for measuring depressive symptom severity from de-identified multi-modal data. The input to our model is audio, 3D video of facial keypoints, and a text transcription of a patient speaking during a clinical interview. The output of our model is either a PHQ score or classification label indicating major depressive disorder. Our method leverages a causal convolutional network (C-CNN) to “summarize" sentences into a single embedding. This embedding is then used to predict depressive symptom severity. In our experiments, we show how our sentence-based model performs in relation to word-level embeddings and prior work.

Figure 1: Multi-modal data. For each clinical interview, we use: (a) video of 3D facial scans, (b) audio recording, visualized as a log-mel spectrogram, and (c) text transcription of the patient’s speech. Our model predicts the severity of depressive symptoms using all three modalities.

2 Dataset

We use the DAIC-WOZ dataset gratch2014distress containing audio and 3D facial scans of depressed and non-depressed patients. For each patient, we are provided with the PHQ-8 score. This corpus is created from semi-structured clinical interviews where a patient speaks to a remote-controlled digital avatar. The clinician, through the digital avatar, asks a series of questions specifically aimed at identifying depressive symptoms. The agent prompts each patient with queries that included questions (e.g. “How often do you visit your hometown?") and conversational feedback (e.g. “Cool."). A total of 50 hours of data was collected from 189 clinical interviews from a total of 142 patients. Following prior work al2018detecting , results in our paper are from the validation set. More details can be found in Appendix A.

Privacy. Data used in this work does not contain protected health information (PHI). Mentions of personal names, specific dates, and locations were removed from the audio recording and transcription by the dataset curators gratch2014distress . The 3D facial scans are low-resolution (68 pixels) and do not contain enough information to identify the individual but contain just enough to measure facial motions such as eye, lip, and head movements. While the dataset is publicly available, future researchers who apply our method to other datasets may encounter PHI and should design their experiments appropriately.

3 Model

Figure 2: Our method: Learning a multi-modal sentence embedding. Overall, our model is a causal CNN bai2018empirical

. The input to our model is: audio, 3D facial scans, and text. The multi-modal sentence embedding is fed to a depression classifier and PHQ regression model (not shown above).

Our model consists of two technical pieces: (i) a sentence-level “summary" embedding and (ii) a causal convolutional network (C-CNN). An overview is shown in Figure 2.

Sentence-Level Embeddings. For decades, word and phoneme-level embeddings222The goal of an embedding

is “summarize" a variable-length sequence as a fixed-size vector of numbers.

have been the go-to feature for encoding text and speech bourlard1990continuous ; bengio2014word ; pennington2014glove ; turian2010word . While these embeddings work well for some tasks sotelo2017char2wav ; kim2016character ; peters2018deep , they are limited in their sentence-level modeling ability. This is because word and phoneme embeddings capture a narrow temporal context, often a few hundred milliseconds at most waibel1990phoneme ; labov200650 . In this work, we propose a novel multi-modal sentence-level embedding. This allows us to capture long-term acoustic, visual, and linguistic elements.

Causal Convolutional Networks.

During clinical interviews, patients may stutter and frequently pause between words. This causes audio-video recordings to be longer than non-depressed patients. Recently, causal convolutional networks (C-CNNs) have been shown to outperform recurrent neural networks (RNNs) on long sequences

bai2018empirical . In miller2018recurrent , the authors even show that RNNs can be approximated by fully feed-forward networks (i.e., CNNs). Combined with dilated convolutions oord2016wavenet , C-CNNs are well-poised to model the long sequences for depression screening interviews. For a more thorough comparison of C-CNNs vs RNNs, we refer the reader to Bai et al. bai2018empirical .

4 Experiments

Our experiments consist of two parts. First, we compare our method to existing works for measuring the severity of depressive symptoms (Table 1). We predict both the PHQ score and output a binary classification as to whether the patient has major depressive disorder, typically with a PHQ score greater than or equal to ten manea2012optimal . Second, we perform ablation studies on our model to better understand the effect of multiple modalities and sentence-level embeddings (Table 2

). Data formats, neural network architectures, and key hyperparameters can be found in Appendix

A.

4.1  Automatically Measuring the Severity of Depressive Symptoms

Classification: Major Depressive Disorder Regression: PHQ Score
# Method Modalities F1 Score Precision Recall Average Error
1 SVM valstar2016avec A 46.2 31.6 85.7 6.74
2 CNN+LSTM ma2016depaudionet A 52.0 35.0 100.0
3 SVM valstar2016avec V 50.0 60.0 42.8 7.13
4 Williamson et al. williamson2016detecting V 53.0 5.33
5 Williamson et al. williamson2016detecting L 84.0 3.34
6 Alhanai et al. al2018detecting AL 77.0 71.0 83.0 5.10
7 SVM valstar2016avec AV 50.0 60.0 42.8 6.62
8 Gong et al. gong2017topic AVL 70.0 2.77
9 Williamson et al. williamson2016detecting AVL 81.0 4.18
10 C-CNN bai2018empirical AVL 76.9 71.4 83.3 3.67
Table 1: Comparison of machine learning methods for detecting depression. Two tasks were evaluated: (i) binary classification of major depressive disorder and (ii) PHQ score regression. Modalities: A: audio, V: visual, L: linguistic (text), AVL: combination. For prior work, numbers are reported from the original publications. Dashes indicate the metric was not reported.

In Table 1, we compare our method to prior work on measuring depressive symptom severity. One difference between our method and prior work is that our method does not rely on interview context. Prior work depends heavily on interview context such as the type of question asked al2018detecting ; gong2017topic , whereas our method accepts a sentence without such metadata. While additional context typically helps the model, it can introduce technical challenges such as having too few training examples per contextual class. Another difference is that our method uses raw input modalities: audio, visual, and text. Prior work uses engineered features such as min/max vocal pitch and word frequencies.

4.2  Ablation Study

In Table 2, rows 1-6 denote hand-crafted or pre-trained sentence-level embeddings. That is, the entire input sentence (audio, 3D facial scans, and transcript) is summarized into a single vector mikolov2013distributed ; le2014distributed ; cer2018universal . However, we propose to learn a sentence-level embedding from the input. These are shown in rows 7 and 8. It is important to note that our method does use hand-crafted and pre-trained word-level embeddings as input. However, internally, oure model learns a sentence-level embedding. Following prior work on sentence-level embeddings, rows 1-6 were computed via simple average cer2018universal

. To learn sentence-level embeddings, we evaluate: (i) long short-term memory

hochreiter1997long and (ii) causal convolutional networks oord2016wavenet ; bai2018empirical .

Classification: Major Depressive Disorder Regression: PHQ Score
Sensitivity (TPR) Specificity (TNR) Average Error
# Method A V L AVL A V L AVL A V L AVL
1 Log-Mel 70.5 64.5 6.40
2 MFCC 74.3 67.7 5.96
3 3D Face openface 71.4 69.4 5.82
4 W2V mikolov2013distributed 65.3 64.6 6.16
5 D2V le2014distributed 67.7 71.4 5.81
6 USE cer2018universal 63.4 62.5 6.27
7 LSTM hochreiter1997long 53.9 61.0 57.5 74.2 54.6 59.7 60.3 76.1 7.15 6.12 6.57 5.18
8 C-CNN bai2018empirical 71.1 73.7 67.7 83.3 66.7 71.2 65.5 82.6 5.78 5.01 6.14 3.67
Table 2: Ablation study. Rows 1-2 are hand-crafted embeddings. Rows 3-6 are pre-trained embeddings. Rows 7-8 denote our learned sentence-level embeddings. Modalities: A: audio, V: visual, L: linguistic (text), AVL: combination. TPR and TNR denote true positive and negative rate, respectively. The input to 7-8 were sequences of log-mel spectrograms, 3D faces, and Word2Vecs.

5 Discussion

Before adapting our work to future research, there are some points to consider. First, although a human was controlling the digital avatar, the data was collected from human-to-computer interviews and not human-to-human. Compared to a human interviewer, research has shown that patients report lower fear of disclosure and display more emotional intensity when conversing with an avatar lucas2014s . Additionally, people experience psychological benefits from disclosing emotional experiences to chatbots ho2018psychological . Second, although it is commonly used in treatment settings and clinical trials, the symptom severity score (PHQ) is not the same as a formal diagnosis of depression. Our work is meant to augment existing clinical methods and not issue a formal diagnosis. Finally, while pre-existing embeddings are easy to use, recent research suggests these vectors may contain bias due to the underlying training data caliskan2017semantics ; bolukbasi2016man ; garg2018word . Mitigating bias is outside the scope of our work, but is crucial to providing culturally sensitive diagnosis and treatment.

Future work could better utilize longitudinal and temporal information such as depression scores across interview sessions that are weeks or months apart. Understanding why the model made certain predictions could also be valuable. Visualizations such as confidence maps over the 3D face and “usefulness" scores for audio segments could shed new insights.

In conclusion, we presented a multi-modal machine learning method which combines techniques from speech recognition, computer vision, and natural language processing. We hope this work will inspire others to build AI-based tools for understanding mental health disorders beyond depression.

Acknowledgements. This work was supported by a National Institutes of Health, National Center for Advancing Translational Science, Clinical and Translational Science Award (KL2TR001083 and UL1TR001085). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Appendix A Appendix

a.1 Data Format

Full data details can be found on the original dataset website baltruvsaitis2016openface . Audio was recorded with a head-mounted microphone at 16 kHz. Video was recorded at 30 frames per second with a Microsoft Kinect. A total of 68 three-dimensional facial keypoints were extracted using OpenFace baltruvsaitis2016openface . Audio was transcribed by the dataset curators and segmented into sentences and phrases with millisecond-level timestamps gratch2014distress . We use the dataset’s train-val split: train (107 patients), validation (35 patients). Note that while a test set exists, the labels are not public.

We canonicalized slang words present in the transcription. For example, bout was translated to about, till was translated to until, and lookin was translated to looking. All text was forced to lower case. Numbers were canonicalized as well (e.g., 24 was represented as twenty four).

a.2 Implementation Details

a.2.1 Experiment 1: Automatically Measuring the Severity of Depressive Symptoms

Input to “our method", i.e. Causal CNN are as follows:

  • [itemsep=0pt,topsep=0pt,leftmargin=20pt]

  • Audio: Log-mel spectrograms with 80 mel filters.

  • Visual: 68 3D facial keypoints.

  • Linguistic: Word2Vec embeddings mikolov2013distributed .

The network architecture is a 10-layer causal convolutional network bai2018empirical

with kernel size of 5 with 128 hidden nodes per layer. Dropout was applied to all non-linear layers with a 0.5 probability of being zeroed. The loss objectives were binary cross entropy for classification and mean squared error for regression. The model was optimized with the Adam optimizer with

and

with L2 weight decay of 1e-4. The initial learning rate was 1e-3 for classification and 1e-5 for regression. A batch size of 16 was used. The model was trained on a single Nvidia V100 GPU for 100 epochs. Our model was implemented with Pytorch

paszke2017automatic .

a.2.2 Experiment 2: Ablation Studies

For Table 2, the details for each row are as follows:

  1. [itemsep=0pt,topsep=0pt,leftmargin=20pt]

  2. Log-mel spectrograms were computed with 80 mel filters.

  3. Mel-frequency cepstral coefficients were computed with 13 resulting values.

  4. A total of 68 three-dimensional facial keypoints were provided by the dataset gratch2014distress . They were extracted using OpenFace openface .

  5. Word2Vec vectors were computed using the publicly available Word2Vec model from Google and the Gensim python library rehurek_lrec . Each vector is of length 300.

  6. Doc2Vec vectors were also computed using Gensim rehurek_lrec . Each vector is of length 300.

  7. Universal sentence embeddings were computed using Tensorflow abadi2016tensorflow from the publicly available release. Each vector is of length 512.

  8. The LSTM consists of 10 layers with 128 hidden units and is also optimized with the same batch size, optimizer, etc. as stated in Appendix A.2.1.

  9. Our causal CNN model is the same as the one outlined in Appendix A.2.1.

Public code implementations333https://github.com/locuslab/TCN were used for the core network architecture components for both the LSTM and causal CNN.