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• Phase 8: Final Report and Presentation

  # Phase 8: Final Report and Presentation ## Duration: 1 Month ### Objectives - Compile and present the research findings. - Prepare a final report and presentation. ### Activities #### Report Compilation - Compile all findings, analyses, and results into a comprehensive final report. - Include discussions on limitations, challenges, and future research directions. #### Presentation Preparation - Prepare a presentation summarizing the research process and key findings. - Present the research to stakeholders and peers. ### Deliverables - Comprehensive final report documenting research findings, analyses, and conclusions. - Professional presentation summarizing key aspects of the research for stakeholders and peers.

  No due date
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• Phase 6: Model Evaluation and Testing

  No due date
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• Phase 5: Model Development

  Overdue by 10 month(s)

  •

  Due by February 28, 2025

  •14/19 issues closed
  73% complete5 open14 closed

• Phase 4: Feature Extraction and Analysis

  # Phase 4: Feature Extraction and Analysis ## Duration: 2 Months ### Objectives - Identify and extract relevant features for emotion recognition from speech data. - Analyze feature importance and suitability for multilingual SER. ### Activities #### Feature Extraction - Utilize wave2vec and waveLM to extract features from speech data. - Evaluate and determine the most effective feature extraction method for emotion recognition. #### Feature Analysis - Perform statistical analysis to understand feature distributions and variances across languages. - Identify the most relevant features for emotion recognition. ### Deliverables - Extracted features for each dataset. - Feature analysis report highlighting key findings and recommendations.

  No due date
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• Phase 2: Dataset Collection and Preprocessing

  # Phase 2: Dataset Collection and Preprocessing ## Duration: 0.5 Months ### Objectives - Collect and preprocess multilingual speech emotion datasets. - Ensure data quality and consistency across languages. ### Activities #### Dataset Collection - Gather existing multilingual emotion datasets (e.g., RAVDESS, IEMOCAP, EMODB, EMOVO, ESD, and CaFE). - Ensure datasets cover a variety of emotions and languages. #### Preprocessing - Normalize audio files to a consistent format and quality. - Remove noise and unwanted artifacts from audio files. - Segment speech data into consistent lengths suitable for analysis. ### Deliverables - Preprocessed multilingual speech emotion datasets. - Documentation of preprocessing methods and dataset characteristics.

  No due date

  •11/12 issues closed
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