1. Background

Start by providing context for your study. This section should introduce your research area broadly, establishing the importance of the subject matter. Gradually dive in to present the specific issues that your research intends to address.

• General Context: Present an overview of the topic. For example, if your research deals with voice-based detection of depression using machine learning techniques, you might begin with a discussion on the rising mental health awareness and the increasing prevalence of depression, underscoring the need for accessible and objective early detection methods (Foulkes and Andrews, 2023).
  • Example: "Depression is a significant global health concern, affecting millions worldwide and impacting various aspects of life (WHO, 2024)."
• Narrow Focus: Transition from general to specific. Discuss key theories, concepts, or trends within your topic that relate to your research.
  • Example: "Traditional methods of diagnosing depression rely on self-reported questionnaires and clinical interviews, which can be subjective and time-consuming. This has led to exploring more objective methods, like voice analysis."
• Current Knowledge State: Summarize prevailing findings in the field. Highlight existing studies, methodologies, and findings that will inform your research.
  • Example: "Research on voice-based depression detection has explored various acoustic features, such as pitch and tone, using machine learning algorithms to classify individuals as depressed or not (Richter et al., 2021)."
• Identify the Problem: Conclude the background section by pinpointing the gap in knowledge or the specific problem your research will address, setting the stage for your research question.
  • Example: "However, this lack of robust understanding limits the development of generalizable voice-based depression detection systems suitable for real-world application."

2. Research Problem

• Clearly articulate the main issue or gap in existing knowledge that your research will address. Briefly describe why this problem is significant and what consequences arise from not addressing it.
  • Example: "The inability to reliably identify signs of depression through voice could delay timely interventions, worsening outcomes for individuals suffering from mental health issues."

3. Aim

• Concisely state the primary goal of your research. This should be a single sentence that is clear and focused, starting with an infinitive verb (e.g., the aim is "to investigate," the aim is "to explore").
  • Example: "This study aims to investigate the application of machine learning techniques in voice analysis to detect early signs of depression based on voice features."

4. Objectives

Outline the specific aims of your research, detailing the steps necessary to achieve your overall aim. Use action verbs and ensure these objectives are SMART (Specific, Measurable, Achievable, Relevant, Time-bound).

Example Objectives:

• To gather and preprocess voice recordings from established depression-related datasets.
• To identify and extract important voice features corresponding to depressive states.
• To implement machine learning models, like SVM and LSTM, and evaluate their performance in detecting depression using voice data.
• To investigate the comparison between voice analysis and standard practices for detecting depression.

5. Research Questions

Formulate clear and focused research questions that guide your study. Each question should provoke inquiry and exploration rather than simple yes or no answers.

Example:

• RQ 1. How can machine learning approaches enhance the recognition of depressive patterns within speech?”
• RQ 2. What specific voice features are most indicative of depressive states?

6. Significance of the Study

Articulate the potential impact of your research. Discuss how your findings could contribute to the field, address real-world problems, and influence policy or practice.

• Contribution: Specify how your research offers new insights or addresses unmet needs.
  • Example: “This research has the potential to revolutionize early depression detection methods by providing an objective, non-invasive solution.”
• Relevance: Highlight the importance of your research in the current academic or practical context.
  • Example: “As mental health issues become increasingly prevalent globally, developing efficient detection methods is significant.”
• Beneficiaries: Identify who will benefit from your research and in what ways.
  • Example: “Mental health practitioners and patients will benefit from more efficient and timely identification of depressive symptoms, potentially improving treatment outcomes.”

7. Literature Review

Provide a comparative overview of existing literature related to your topic. Summarize key studies, methodologies, and findings, highlighting how they relate to your research question.

• Key Themes: Identify the main themes or debates in the literature.
• Critical Analysis: Discuss strengths and weaknesses in existing research, leading to the identification of gaps your study aims to fill.

8. Gaps

Pinpoint specific gaps in existing literature that your research will address. Clearly outline why these gaps are significant and how your study will contribute to filling them.

• Example: “This study aims to bridge the gap by implementing advanced machine learning models to improve the objective detection of depression through voice analysis.”

9. Methodology

Outline the methods you will use to achieve your research objectives. Include details about your research design, data collection methods, and analytical techniques.

• Design: Specify if your research is qualitative, quantitative, or mixed methods.
  • Example: “A mixed methods approach will be employed, combining quantitative analysis of voice data with qualitative assessments of participant experiences.”
• Data Collection: Describe how you will gather data (e.g., surveys, experiments, literature review)
  • Example: “Data will be sourced from established depression-related datasets and supplemented by audio recordings of participant interviews.”
  • Analysis: Explain the methods of analysis you will employ (e.g., statistical analysis, thematic analysis). Example: "Performance metrics for machine learning models will be evaluated using precision, recall, and F1 scores to determine effectiveness in depression detection."

10. Project Timeline

Provide a timeline for your research, breaking down tasks into manageable segments.

• Work Breakdown Structure (WBS); Outline specific phases of the project, detailing tasks and milestones by using a project management software such as Microsoft Project or Miro.
• Gantt Chart: Here a screenshot of Gantt Chart, Critical Path, or Kanban will be attached.

11. Ethical Considerations

Discuss ethical issues related to your research, including how you plan to handle them. Consider aspects such as informed consent, confidentiality, and the impact of your research on participants and the environment.

• Example: "All participants will provide informed consent, ensuring confidentiality of their responses and recordings, with the right to withdraw at any time."

12. References

List all the academic work cited in your proposal following a standard citation style (e.g., Harvard, APA, MLA). Ensure that your references are current and relevant to your topic.

Example:

• Foulkes, L. and Andrews, J.L. (2023). Are mental health awareness efforts contributing to the rise in reported mental health problems? A call to test the prevalence inflation hypothesis. New Ideas in Psychology, [online] 69(1), p.101010. doi:https://doi.org/10.1016/j.newideapsych.2023.101010.
• Richter, T., Fishbain, B., Richter-Levin, G. and Okon-Singer, H. (2021). Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. Journal of Personalized Medicine, 11(10), p.957. doi:https://doi.org/10.3390/jpm11100957.