Depressive and mania mood state detection through voice as a biomarker using machine learning

Introduction: Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA).

Results: Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%.

Discussion: These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.

1 Introduction

The World Health Organization (WHO) estimates that 40 million people had bipolar disorder (BPD) and 280 million people suffered from depression in 2019 (1). Depression, sometimes referred to as unipolar depression or major depressive disorder (MDD), is a mental illness that affects a person’s everyday functioning and is characterized by recurrently low emotions or a loss of interest in activities. It might be challenging to differentiate bipolar illness from depression because of its repeated episodes of mania/hypomania and depression (2). In China, the lifetime prevalence rates of bipolar disorder and depression are 0.6% and 6.8%, respectively (3), and the prevalence is still rising as a result of COVID-19 (4). Mental disorder can increase the risk of suicide. People with depression are 20 times more likely to commit suicide (5). Depression has become the fourth leading cause of death (6). Patients with manic episodes tend to exhibit elevated mood, a tendency to anger easily, and excessive sensitivity. Over time, these conditions can lead to physical fatigue and compromised immune systems. Furthermore, they may engage in high-risk activities due to impulsiveness, posing potential harm to themselves and society.

At present, the detection of depression and manic mood are usually made by observer-based clinical rating scales, such as the Mood Disorder Questionnaire (MDQ), Quick Inventory of Depressive Symptomatology (QIDS), and Young Mania Rating Scale (YMRS). These scales are used as gold standards to evaluate the severity of manic and depressive symptoms, which are based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) (7). Certainly, the precision of these rating scales depends on participant compliance and the subjective interpretation of practitioners. Therefore, developing continuous, objective assessments of symptom severity would be groundbreaking.

In recent years, many machine learning and deep learning methods are used to automatic mood recognition by speech. Faurholt-Jepsen et al. (8) presented voice analysis as an objective state marker in bipolar disorder in 2016, however the study only included the speech features of 28 participants. Shin et al. (9) suggested a speech biomarker machine learning model for the identification of moderate and serious depression in 2021. Lin et al. (10) proposed a deep learning method for diagnosing depressive orders; Punithavathi et al. (11) conducted an empirical investigation that demonstrated the potential of machine learning-based voice recognition techniques for depression prediction; and Shen et al. (12) proposed a GRU/BiLSTM-based model for depression detection. The advantages and disadvantages of these studies are shown in Table 1.

Table 1

Table 1. Previous studies and their advantages and disadvantages.

The Transformer model has gained popularity in recent years, first appearing in textual analysis to handle long-range dependencies in text. Since its introduction by Vaswani et al. (13), the model has made great progress in both speech and text recognition. Zhang and colleagues (14) presented a hybrid model for depression detection that combines Transformers and BiLSTM. Moreover, BERT is a pre-trained Transformer model for text analysis that was introduced by Devlin et al. (15). Using Wav2Vec 2.0 (16) that has already been trained, Banno et al. (17) devised a method for assessing oral proficiency in English. Guo et al. (18) developed a pre-trained model appropriate for Chinese in 2022. Therefore, in voice recognition, pre-trained models are now commonly employed, providing novel methods for recognizing emotions.

While speech is a useful tool for categorizing depression and manic episodes in these models, their drawbacks include a lack of characteristics and insufficient examination of the transitions between these states. This study suggests three models to differentiate between depression, mania, and remission emotions in order to address these constraints. It also contrasts these models to find out if using pre-trained models improves classification accuracy. This study developed a voice-based machine learning model for diagnosing mania and depression by analyzing 1,337 voice messages from 93 participants. We suggest that voice can function as an objective marker for supplementary diagnosis of emotional states by the analysis of objective speech data, allowing for the predictive forecasting of emotional phase transitions.

2 Materials and methods

2.1 Data sample

A self-monitoring app named MoodMirror was loaded onto the participants’ smartphones, and an alarm was set to sound once a day at a time of their choosing to remind the patients to provide electronic self-monitored data. The participants completed all assessment questionnaires, verified their informed consent, and obtained trial information via the app.

In this current longitudinal investigation, the following hypotheses were tested using the MoodMirror system in participants presenting with moderate to severe degrees of manic and depressed symptoms: In naturalistic settings, voice features from ordinary life that were extracted using the “mood diary” module may distinguish between different affective states. Participants are asked to describe their present naturalistic mood state in the “mood diary” module stated above. Participants’ smartphones recorded audio at a sample rate of 16 kHz.

Every participant was chosen from Peking University Sixth Hospital and provided written informed consent electronically via the MoodMirror app. Based on the subjects’ current depressive and manic states, a sample of 1,337 voice messages from 93 subjects was divided into three groups: the depression mood state group (431 voice messages from n = 39), the mania mood state group (208 voice messages from n = 20), and the remission group (698 voice messages from n = 34). The demographic information of the participants is shown in Table 2.

Table 2

Table 2. Comparing the demographics based on various emotional states.

2.2 Clinical assessments

The MoodMirror app incorporates three self-evaluated scales that are considered gold standards for assessing manic and depressed mood states: the Mood Disorder Questionnaire (MDQ) (19), the Quick Inventory of Depressed Symptomatology (QIDS) (20) and the Young Mania Rating Scale (YMRS) (21). Concurrently, the voice recordings were gathered within the application. Table 3 displays the various combinations of cut-offs that were used to determine the remission, depressive, and manic mood states. Table 4 provides an illustration of the detailed scare scores. ANOVA test was used to compare score differences among three groups.

Table 3

Table 3. Standard of many emotional states.

Table 4

Table 4. Clinical traits according to various emotional states.

2.3 Voice features

In this study, we used a total of 22 voice features: 8 time-domain features (zero-crossing rate, short-term energy, short-term energy entropy, spectral centroid, spectral spread, spectral entropy, spectral flux, spectral rolloff), 13 Mel-Frequency Cepstral Coefficients (MFCC) features, and the duration of the voice recording. Speech contains rhythm and tempo, and longer segments provide more audio information. Thus, feature extraction from longer speech segments captures crucial information more precisely. Longer speeches allow for more fine-grained segments during short-time framing, leading to more accurate information extraction. Consequently, speech duration impacts speech classification, and we included it as a feature in our analysis. The specific meanings of each feature are presented in Table 5.

Table 5

Table 5. Conception of features.

In general, a smaller value of spectral centroid indicated that the spectral energy of the audio signal was more concentrated in the low-frequency range. A greater spectral spread indicated a wider distribution of spectral energy across the frequency domain in the audio signal. Spectral rolloff represented the frequency that was below a specified percentage of the total spectral energy. Mel-Frequency Cepstral Coefficients (MFCC) was a commonly used feature extraction technique in speech and audio signal processing. By extracting MFCCs, audio signals were transformed into a compact set of feature vectors, which could be more easily utilized for tasks such as classification, recognition, or other tasks using machine learning algorithms.

The above-mentioned features id1–id21 were extracted from each frame, then, the average of all frame features was calculated to serve as the feature representation of the speech. The duration of speech was also the feature. Utilizing χ² tests for detecting differences between groups, we selected features with significant differences to train the model. Table 6 shows the inter-group comparison of features for the three categories. The violin plots representing the distributions of three groups of speech features are shown in Supplementary File 1. Features were extracted using the open-source program pyAudioAnalysis (23) of Python version 3.6.1.

Table 6

Table 6. Differences in features of three types of speech.

2.4 Machine learning models

After the feature extraction process from the original speech recordings, leave-one-subject-out was used validation method. The Gate Recurrent Unit (GRU) (12) is a gating mechanism in recurrent neural networks (24), which is similar to LSTM (long short-term memory) (25) with an output gate and fewer parameters. GRU is included in this study since it performs similarly to LSTM with fewer parameters on tasks including speech signal modeling and natural language processing (26, 27).

Bidirectional Long Short-Term Memory (BiLSTM) (28) is a further development of LSTM and BiLSTM combines the forward hidden layer and the backward hidden layer, which can access both the preceding and succeeding information (28). The application of BiLSTM in speech processing typically involves leveraging its bidirectional recurrent structure to capture temporal information in speech signals, thereby improving performance in tasks such as speech recognition and speech emotion recognition.

Wav2vec2.0 (16) is a speech feature extraction model proposed by the Facebook AI Research team, which utilizes Transformer as its underlying architecture. The structure of transformers is illustrated in Figure 1. The Chinese-speech-pretrain model used in this study is developed by TencentGames and Tencent Zhiji based on Wav2vec2.0, which has been trained using a large amount of unsupervised learning data. These data primarily originate from YouTube and Podcast, covering a wide range of recording scenarios, background noises and speaking styles. The model was trained using the Fairseq toolkit (29) and employed the 10,000 h Chinese dataset from the train_1 set of WenetSpeech (30) as the foundation. This self-supervised learning approach enables the model to learn the deep structure of speech without annotations, enhancing its comprehension of Chinese speech.

Figure 1

Figure 1. The structure of transformer.

Linear Discriminant Analysis (LDA) is a linear learning algorithm originally proposed by Fisher (31) in the field of classification. For binary classification problems, LDA projects both categories of data onto a single line to ensure that data within the same category are as close as possible, while data from different categories are as far apart as possible, which means minimizing the intra-class distance and maximizing the inter-class distance. When a new sample is encountered, it is also mapped onto this line, and the category of the new sample is determined based on the position of its projection point (32).

2.5 Processing

First of all, voice recordings and questionnaire data (QIDS, MDQ, and YMRS scores) were collected from 93 patients. The questionnaire responses were organized according to Table 3 criteria to classify patients into depressive, manic, or remission states based on their mood. To mitigate environmental noise variability in self-recorded voices, a band-pass filter (200–4,000 Hz) was applied to improve the signal-to-noise ratio (SNR). The Kruskal-Wallis test was used to compare SNR differences among the three speech groups, while the Mann–Whitney U test was employed to analyze differences between any two speech types. Furthermore, an endpoint detection method based on short-time energy and zero-crossing rate was implemented to remove silent intervals at the beginning and end of each speech sample. Subsequently, speech signals were segmented into frames with a frame duration of 30 ms and a 15 ms frame shift. Each frame underwent Hamming windowing to prepare for subsequent feature extraction.

Initially, the 93 participants were numbered from 1 to 93, and voice features were extracted for each individual. The leave-one-subject-out cross-validation method was employed for model validation. Specifically, the voice data of participant ID1 served as the test set, while the data from the remaining 92 participants constituted the training set. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during training data preparation. For evaluating the models trained using Linear Discriminant Analysis (LDA), Gate Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory (BiLSTM), the voice data from participant ID1 was input into each trained model. The accuracy, specificity, and sensitivity of these models in recognizing voice emotions were computed accordingly. Subsequently, participant ID2’s voice data was used as the test set, and the process of training the models with the data from the remaining 92 participants was repeated. This cycle was iterated until each of the 93 participants had served as the test set at least once, allowing for a comprehensive evaluation of the trained models. Finally, the average performance across all 93 test iterations was calculated to determine the overall effectiveness of the models.

In addition, default parameters were used in LDA. And for GRU and BiLSTM, grid search was employed to determine the optimal parameters. The parameters to be determined include batch-size, with options of 30, 40, 50; dropout rate with options of 0.1, 0.2, 0.3; and learning rate with options of 0.006, 0.0006, 0.00006. Both GRU and BiLSTM use ReLU and Softmax as the activation functions for the fully connected layers. The aforementioned training process was implemented using PyTorch (33). As a result, the batch-size, dropout and learning rate of GRU were 40, 0.3 and 0.0006, respectively, while for BiLSTM, the batch-size, dropout and learning rate were 40, 0.2 and 0.0006, respectively.

This study retrieved features using the pre-trained model Wav2vec2.0, and the best results were obtained by training using the aforementioned ideal parameters and model.

3 Results

Table 2 presents demographic information. Gender, marital status, years of schooling, and age showed significant differences (p < 0.05) across the three groups.

Table 4 illustrates that there are differences among the three groups of people in the three scales of MDQ, QIDS and YMRS (p < 0.05).

The average signal-to-noise ratio (SNR) for depressive mood speech is 5.838 ± 3.211, while the average SNR for manic mood speech is 5.586 ± 3.211, and the SNR for the speech during remission period is 4.561 ± 2.281. Significant differences in SNR were found among these groups (K = 143.954, p < 0.05). Specifically, there was no significant difference in SNR between depressive and manic mood speech (U = 42,872, p = 0.379). However, SNR differed significantly between the remission period and depressive mood speech (U = 101,271, p < 0.05), as well as between the remission period and manic mood speech (U = 38,077, p < 0.05).

As is shown in Figure 2, the speech length of people in the depressive mood state is concentrated in 40–60 s, the speech length of the manic mood state is concentrated in 1–30 s, and the speech length of the people in the remission is concentrated in 20–40 s. The speech duration typically ranges from 1 to 60 s, with graphical representation continuing beyond 60 s. This extension occurs due to a peak near the 60-s mark, where the graph automatically extends around the peak instead of abruptly stopping. The duration of speech varies with different emotions, which correlates with emotional states. Individuals experiencing depression often communicate via electronic devices like mobile phones, showing a strong inclination to express themselves (34, 35). They may exhibit slower speech due to lower mood and energy levels, along with reduced interaction with others. This slower articulation could stem from delayed thought processes. Conversely, individuals in a manic state tend to speak rapidly, reflecting impatience and haste, resulting in shorter recorded speech durations.

Figure 2

Figure 2. Density map showing the dispersion of voice recording duration.

Moreover, Figure 3 shows participants’ Mel spectrograms depressive, manic and remission mood states. In a Mel spectrogram, time is depicted on the horizontal axis and frequency on the vertical axis, with brighter areas indicating higher energy levels. The figure illustrates that the sound energy across the three different emotional states is primarily concentrated in lower frequencies. Specifically, the depressive state and remission period exhibit concentration below 2,048 Hz, while the manic mood is concentrated below 1,024 Hz. A comparative analysis reveals that compared to the remission period, the energy distribution in the depressive state is relatively more dispersed, while in the remission period, energy is denser than 512 Hz. This observation highlights discernible distinctions in energy and frequency among the three emotional states, thereby facilitating the discrimination of speech emotions. As can be seen in Figure 3, the MFCC plots of the depressive and the remission are similar, while the manic mood state is significantly different from them. This might be attributed to depression and mania being two extremes of emotion, thus potentially exhibiting starkly different patterns in speech features.

Figure 3

Figure 3. Participants’ Mel spectrograms showing depressive, manic and remission mood states.

Table 6 demonstrates the contrasting differences of 22 features among depressive mood, manic mood, and remission mood voices. It is evident from Table 6 that, except for the MFCC6 feature, which showed no significant difference among the three voice types (p = 0.248), the remaining 21 features exhibited significant differences (p < 0.001). Supplementary File 1 presents violin plots of the 21 features excluding MFCC6, which indicate variations in the distributions of these 21 features across the three types of voice. For detailed information, please refer to Supplementary File 1. These 21 features are the selected ones for model training.

Lastly, by using the selected 21 features to train the model, the confusion matrix and performance of the model is shown in Tables 7, 8. The Chinese-speech-pretrain-GRU achieves an accuracy of 80.2%, outperforming LDA (78.9%), BiLSTM (69.5%), and GRU (70.0%). BiLSTM demonstrates 82.2% specificity and 56.4% sensitivity for detecting depressive mood, and 85.3% specificity and 46.2% sensitivity for manic mood. 60.1% precision and 58.2% F1-score for detecting depressive mood, and 36.6% precision and 40.8% F1-score for manic. Similarly, GRU exhibits 57.3% sensitivity, 82.4% specificity, 60.8% precision and 59.0% F1-score for depressive mood, and 56.3% sensitivity, 86.2% specificity, 42.8% precision and 48.6% F1-score for manic mood.

Table 7

Table 7. The confusion matrix of LDA, BiLSTM, GRU and Chinese-speech-pretrain+GRU.

Table 8

Table 8. Performance comparison between LDA, BiLSTM, GRU, and Chinese-speech-pretrain+GRU.

Further, for depressed conditions, the Chinese-speech-pretrain-GRU shows 77.5% sensitivity, 86.1% specificity, 75.0% precision and 72.6% F1-score, and for manic states, 54.8% sensitivity, 90.3% specificity, 52.9% precision and 51.1% F1-score. LDA, in contrast, reaches 62.5% sensitivity and 89.7% specificity, 52.8% precision and 57.2% F1-score for manic states and 70.3% sensitivity, 89.8% specificity, 76.7% precision and 73.4% F1-score for depressed conditions. LDA performs better than BiLSTM and GRU, but Chinese-speech-pretrain-GRU performs better overall. Table 7 shows the confusion matrix of four model. Table 8 presents the specific findings.

4 Discussion

This study shows that Chinese-speech-pretrain-GRU, BiLSTM, LDA and GRU can be utilized for voice analysis of mood state detection, and the Chinese-speech-pretrain-GRU can distinguish depressive and manic mood with 80.2% accuracy, the LDA can distinguish depressive and manic mood with 78.9% accuracy, the GRU can distinguish depressive and manic mood with 70%, while the BiLSTM with 69.5% accuracy. It means machine learning models can distinguish those mood states through objective speech, and pre-train models can further extract the information embedded in the speech, thus improving prediction accuracy. Furthermore, speech can serve as a biomarker to differentiate between depressive and manic moods.

In recent years, several scholars have explored multimodal models for identifying depressive and manic emotions. Ye et al. (36) introduced a hybrid model integrating voice and text for depression detection. Zheng et al. (37) proposed a multitask model capable of simultaneous emotion recognition and depression detection. Alghowinem et al. (38) developed a machine learning-based multimodal depression detector, showing significant advancements over unimodal approaches. Future studies could involve not only voice but also text, video, and image modalities. This extension may provide a thorough comprehension of differences between manic, depressed, and remission phases from many angles, improving the precision of emotion identification.

Patients with bipolar disorder have the characteristics of “polarization” of emotions. The methods and focuses of interventions for manic and depressive episodes are different. During manic episodes, it is necessary to prevent the harm caused by mania. However, during a depressive episode, we need to pay attention to the risk of self-injury and suicide, and consider using antidepressants in combination (39). By integrating a model that can effectively identify different phases from mobile devices such as mobile phones, and having patients or their family members regularly upload their voices, remote and regular monitoring of the emotional phases of bipolar patients can be achieved, so that intervention measures and interventions for patients can be adjusted in a timely manner. The treatment plan provides a feasible solution to achieve personalized treatment of bipolar disorder and save the human and material costs required for mental illness management. This study also has certain auxiliary value in the diagnosis of bipolar disorder. Because patients are lack of knowledge for themselves disease and may be in a depressive episode when seeking treatment, bipolar disorder is easily misdiagnosed as unipolar depression. The ideas proposed in this study can help doctors discover missed manic phases in clinical practice.

When the sound wave in the voice is affected by aerodynamic factors and generates mechanical vibration, it is converted into a sound source signal to generate (40). The voice contains biological acoustic characteristics such as spectrum, prosody, and formant (41, 42). The length of the voice has an impact on the amount and accuracy of the features contained in the voice. Vogel and Morgan studied that the length of voice data would affect the accuracy of biological characteristics in the voice (43). Scherer et al. showed through research that the accuracy of disturbance measurement in the voice was affected by the duration of the voice, and only voice over 3 s could provide accurate features (44). There are also studies proving that the pitch measurement of long voice is more accurate than that of short voice (45, 46). To conclude, in this study, we observed that patients with different emotional states exhibited varying voice durations, indicating that the information carried in their voices differs. Significant differences in voice durations were found among patients in depressive, manic, and remission states. However, we did not study the impact of voice duration on the internal characteristics of speech. Future research will further analyze the specific impact of duration on these speech characteristics.

This study also has certain limitations. Firstly, the number of participants included in this study is limited, and the use of self-reported scales may lead to inaccuracies of the emotion labels. Secondly, regarding the quantity of speech data, the number of recordings varies among patients, which may introduce bias to the model. Thirdly, in the study, we only used voice as a biomarker to investigate the recognition of emotions. Therefore, in order to enhance the accuracy and generalizability of the results, the future study will increase the number of participants, control the quantity of speech samples per patient, and utilize alternative techniques to ascertain the patients’ emotional states. In subsequent research, apart from speech, we will introduce information such as images, videos and texts to detect depressive mood, manic mood and remission emotion through a Multi-Modal machine learning approach.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Medical Ethics Committee of Peking University Sixth Hospital (Institute of Mental Health). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

JJ: Writing – original draft. WD: Conceptualization, Supervision, Writing – review & editing. JL: Methodology, Writing – original draft. JP: Methodology, Writing – original draft. CF: Data curation, Writing – review & editing. RL: Data curation, Writing – review & editing. CS: Writing – review & editing. YM: Conceptualization, Funding acquisition, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The work was supported by the Capital’s Funds for Health Improvement and Research (grant numbers 2018-2-4112 and 2020-2Z-4117); the Capital Characteristics of Clinical Application Research provided by the Beijing Municipal Science and Technology Commission (grant number Z171100001017086); and the Space Medical Experiment Project of China Manned Space Program (grant number HYZHXM03006).

Acknowledgments

Thanks to all the researchers who participated in this study.

Conflict of interest

JJ and CF were employed by Beijing Wanling Pangu Science and Technology Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2024.1394210/full#supplementary-material

References

1. Global Health Data Exchange (GHDx) . Available at: https://vizhub.healthdata.org/gbd-results/ (2019).

Google Scholar

2. Phillips, ML, and Kupfer, DJ. Bipolar disorder diagnosis: challenges and future directions. Lancet. (2013) 381:1663–71. doi: 10.1016/S0140-6736(13)60989-7

PubMed Abstract | Crossref Full Text | Google Scholar

3. Huang, Y, Wang, YU, Wang, H, Liu, Z, Yu, X, Yan, J, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry. (2019) 6:211–24. doi: 10.1016/S2215-0366(18)30511-X

Crossref Full Text | Google Scholar

4. Zhang, J, Lu, H, Zeng, H, Zhang, S, Du, Q, Jiang, T, et al. The differential psychological distress of populations affected by the COVID-19 pandemic. Brain Behav Immun. (2020) 87:49. doi: 10.1016/j.bbi.2020.04.031

Crossref Full Text | Google Scholar

5. Lépine, JP, and Briley, M. The increasing burden of depression. Neuropsychiatr Dis Treat. (2011) 7:3–7. doi: 10.2147/ndt.s19617

PubMed Abstract | Crossref Full Text | Google Scholar

6. Wu, P, Wang, R, Lin, H, Zhang, F, Tu, J, and Sun, M. Automatic depression recognition by intelligent speech signal processing: a systematic survey. CAAI Trans Intell Technol. (2023) 8:701–11. doi: 10.1049/cit2.12113

Crossref Full Text | Google Scholar

7. Silverman, MA . Diagnosing the diagnostic and statistical manual of mental disorders. By Rachel Cooper. Psychoanal Q. (2015) 84:239–47. doi: 10.1002/j.2167-4086.2015.00011.x

Crossref Full Text | Google Scholar

8. Faurholt-Jepsen, M, Busk, J, Frost, M, Vinberg, M, Christensen, EM, Winther, O, et al. Voice analysis as an objective state marker in bipolar disorder. Transl Psychiatry. (2016) 6:e856–6. doi: 10.1038/tp.2016.123

PubMed Abstract | Crossref Full Text | Google Scholar

9. Shin, D, Cho, WI, Park, CHK, Rhee, SJ, Kim, MJ, Lee, H, et al. Detection of minor and major depression through voice as a biomarker using machine learning. J Clin Med. (2021) 10:3046. doi: 10.3390/jcm10143046

PubMed Abstract | Crossref Full Text | Google Scholar

10. Lin, Y, Liyanage, BN, Sun, Y, Lu, T, Zhu, Z, Liao, Y, et al. A deep learning-based model for detecting depression in senior population. Front Psych. (2022) 13:1016676. doi: 10.3389/fpsyt.2022.1016676

PubMed Abstract | Crossref Full Text | Google Scholar

11. Punithavathi, R, Sharmila, M, Avudaiappan, T, Raj, I, Kanchana, S, and Mamo, SA. Empirical investigation for predicting depression from different machine learning based voice recognition techniques. Evid Based Complement Alternat Med. (2022) 2022:1–9. doi: 10.1155/2022/6395860

Crossref Full Text | Google Scholar

12. Shen, Y., Yang, H., and Lin, L. Automatic depression detection: an emotional audio-textual corpus and a GRU/BiLSTM-based model. In ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2022), 6247–6251.

Google Scholar

13. Vaswani, A, Shazeer, N, Parmar, N, Uszkoreit, J, Jones, L, Gomez, AN, et al. Attention is all you need. Adv Neural Inf Proces Syst. (2017) 30:15. doi: 10.48550/arXiv.1706.03762

Crossref Full Text | Google Scholar

14. Zhang, Y, He, Y, Rong, L, and Ding, Y A hybrid model for depression detection with transformer and bi-directional long short-term memory. In: 2022 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, (2022): 2727–2734.

Google Scholar

15. Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. (2018). arXiv [Preprint]. arXiv: 1810.04805.

Google Scholar

16. Alexei, B., Zhou, H., Abdelrahman, M, and Michael, A, "wav2vec 2.0: A framework for self-supervised learning of speech representations," In: Proc. NeurIPS, (2020).

Google Scholar

17. Bannò, S., and Matassoni, M., Proficiency assessment of L2 spoken English using Wav2Vec 2.0. In: 2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar, (2023), pp. 1088–1095.

Google Scholar

18. TencentGames . Available at: https://github.com/TencentGameMate/chinese_speech_pretrain (2022)

Google Scholar

19. Hirschfeld, RM, Williams, JB, Spitzer, RL, Calabrese, JR, Flynn, L, Keck, PE Jr, et al. Development and validation of a screening instrument for bipolar spectrum disorder: the mood disorder questionnaire. Am J Psychiatry. (2000) 157:1873–5. doi: 10.1176/appi.ajp.157.11.1873

PubMed Abstract | Crossref Full Text | Google Scholar

20. Rush, AJ, Bernstein, IH, Trivedi, MH, Carmody, TJ, Wisniewski, S, Mundt, JC, et al. An evaluation of the quick inventory of depressive symptomatology and the Hamilton rating scale for depression: a sequenced treatment alternatives to relieve depression trial report. Biol Psychiatry. (2006) 59:493–501. doi: 10.1016/j.biopsych.2005.08.022

PubMed Abstract | Crossref Full Text | Google Scholar

21. Young, RC, Biggs, JT, Ziegler, VE, and Meyer, DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. (1978) 133:429–35. doi: 10.1192/bjp.133.5.429

Crossref Full Text | Google Scholar

22. Al-Anzi, FS, and Abu, ZD. The capacity of mel frequency cepstral coefficients for speech recognition. Int J Comput Informat Eng. (2017) 11:1149–53. doi: 10.5281/zenodo.1132455

Crossref Full Text | Google Scholar

23. Giannakopoulos, T . Pyaudioanalysis: an open-source python library for audio signal analysis. PLoS One. (2015) 10:e0144610. doi: 10.1371/journal.pone.0144610

PubMed Abstract | Crossref Full Text | Google Scholar

24. Polikar, R. Ensemble based systems in decision making. In: IEEE Circuits and Systems Magazine, (2006), 6, 21–45.

Google Scholar

25. Hermans, M, and Schrauwen, B. Training and analysing deep recurrent neural networks. Adv Neural Inf Proces Syst. (2013) 26:633. doi: 10.5555/2999611.2999633

Crossref Full Text | Google Scholar

26. Gers, FA, Schmidhuber, J, and Cummins, F. Learning to forget: continual prediction with LSTM. Neural Comput. (2000) 12:2451–71. doi: 10.1162/089976600300015015

PubMed Abstract | Crossref Full Text | Google Scholar

27. Ravanelli, M, Brakel, P, Omologo, M, and Bengio, Y. Light gated recurrent units for speech recognition. IEEE Trans Emerg Top Comput Intell. (2018) 2:92–102. doi: 10.1109/TETCI.2017.2762739

Crossref Full Text | Google Scholar

28. Liu, G, and Guo, J. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing. (2019) 337:325–38. doi: 10.1016/j.neucom.2019.01.078

Crossref Full Text | Google Scholar

29. Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., et al. Fairseq: a fast, extensible toolkit for sequence modeling. In: Proceedings NAACL, (2019).

Google Scholar

30. Zhang, B., Lv, H., Guo, P., Shao, Q., Yang, C., Xie, L., et al. WenetSpeech: A 10000+ hours multi-domain mandarin corpus for speech recognition. In: Proceedings ICASSP, (2021).

Google Scholar

31. Fisher, RA . The use of multiple measurements in taxonomic problems. Ann Eugenics. (1936) 7:179–88. doi: 10.1111/j.1469-1809.1936.tb02137.x

Crossref Full Text | Google Scholar

32. Chaonan, F . Optimization and application of psychiatric scale tools based on machine learning Qingdao University (2020). This is a dissertation that can be found on CNKI (China National Knowledge Infrastructure).

Google Scholar

33. PyTorch . Available at: https://pytorch.org (2017)

Google Scholar

34. Shuhong, Q, Ying, C, and Shiwei, H. Relationship among depressive symptoms, neuroticism and network social activites in college students. Chin Ment Health J. (2019) 33:932–7. doi: 10.3969/j.issn.1000-6729.2019.12.009

Crossref Full Text | Google Scholar

35. Bonetti, L, Campbell, MA, and Gilmore, L. The relationship of loneliness and social anxiety with children’s and adolescents’ online communication. Cyberpsychol Behav Soc Netw. (2010) 13:279–85. doi: 10.1089/cyber.2009.0215

Crossref Full Text | Google Scholar

36. Ye, J, Yu, Y, Wang, Q, Li, W, Liang, H, Zheng, Y, et al. Multi-model depression detection based on emotional audio and evaluation text. J Affect Disord. (2021) 295:904–13. doi: 10.1016/j.jad.2021.08.090

PubMed Abstract | Crossref Full Text | Google Scholar

37. Zheng, W, Yan, L, and Wang, F. Two birds with one stone: knowledge-embedded temporal convolutional transformer for depression detection and emotion recognition. IEEE Trans Affect Comput. (2023) 14:2595–613. doi: 10.1109/TAFFC.2023.3282704

Crossref Full Text | Google Scholar

38. Alghowinem, S, Goecke, R, Wagner, M, Epps, J, Hyett, M, Parker, G, et al. Multimodel depression detection:fusion analysis of paralinguishtic, head pose and eye gaze behaviors. Trans Affect Comput. (2018) 9:478–90. doi: 10.1109/TAFFC.2016.2634527

Crossref Full Text | Google Scholar

39. Yatham, LN, Kennedy, SH, Parikh, SV, Schaffer, A, Bond, DJ, Frey, BN, et al. Canadian network for mood and anxiety treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) 2018 guidelines for the management of patients with bipolar disorder. Bipolar Disord. (2018) 20:97–170. doi: 10.1111/bdi.12609

Crossref Full Text | Google Scholar

40. Farouk, M. H. Speech production and perception in application of wavelets in speech Processing. Switzerland Springer, (2018), 5–10.

Google Scholar

41. Cummins, N, Baird, A, and Schuller, BW. Speech analysis for health: current state-of-the-art and the increasing impact of deep learning. Methods. (2018) 151:41–54. doi: 10.1016/j.ymeth.2018.07.007

PubMed Abstract | Crossref Full Text | Google Scholar

42. Akkaralaertsest, T., and Yingthawornsuk, T. Classification of depressed speech samples with spectral energy ratios as depression indicator. In: Proceedings 14th International Joint Symposium Artificial Intelligence Natural Language Processing. (iSAI-NLP), (2019), 12: 1–6.

Google Scholar

43. Vogel, A, and Morgan, A. Factors affecting the quality of sound recording for speech and voice analysis. Speech Lang Pathol. (2009) 11:431–7. doi: 10.3109/17549500902822189

PubMed Abstract | Crossref Full Text | Google Scholar

44. Scherer, RC, Vail, VJ, and Guo, CG. Required number of tokens to determine representative voice perturbation values. Speech Lang Hearing Res. (1995) 38:1260–9. doi: 10.1044/jshr.3806.1260

PubMed Abstract | Crossref Full Text | Google Scholar

45. Zraick, RI, Birdwell, KY, and Smith-Olinde, L. The effect of speaking sample duration on determination of habitual pitch. J Voice. (2005) 19:197–201. doi: 10.1016/j.jvoice.2004.01.010

PubMed Abstract | Crossref Full Text | Google Scholar

46. Almaghrabi, SA . The reproducibility of bio-acoustic features is associated with sample duration, speech task, and gender. IEEE Trans Neural Syst Rehabil Eng. (2022) 30:167–75. doi: 10.1109/TNSRE.2022.3143117

PubMed Abstract | Crossref Full Text | Google Scholar