German Italian Speech Analysis

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : German Italian Speech Analysis :red_circle: Aim : Analyse and visualize different aspects of the German and Italian language. :red_circle: Dataset : https://www.kaggle.com/datasets/mitishaagarwal/german-italian-speech-analysis :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.
• This issue is only for 'SWOC' and 'JWOC' contributors of 'ML-Crate' project.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[guneshm11]

Hello I would like to work on this project. Please assign it to me Full name : Gunesh Mahajan GitHub Profile Link : https://github.com/guneshm11 Participant ID (If not, then put NA) : NA Approach for this Project : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model. What is your participant role? - Contributor

[abhisheks008]

Work on one issue at a time.

[nkhanna94]

Full name: Niharika Khanna GitHub Profile Link: nkhanna94 Participant ID (If not, then put NA): NA

Approach for this Project:

Exploratory Data Analysis (EDA): Overview: Understand dataset structure and language distribution. Feature Extraction: Extract relevant speech features. Visualization: Explore differences in speech characteristics between German and Italian.

Preprocessing: Cleaning: Address missing values, outliers, and noise. Normalization: Normalize extracted features for consistency.

Model Implementation: Algorithm Selection: Employ 3-4 algorithms like CNNs, RNNs, SVMs, and GBMs. Training: Train models using extracted features and language labels.

Model Evaluation: Metrics: Assess model performance using accuracy and cross-validation. Comparison: Identify best algorithm for language classification. Interpretation: Analyze key features contributing to classification.

Participant Role: SSOC S3

[abhisheks008]

Implement 5-6 models for this project.

Assigned @nkhanna94