lou-ukw / indivsprech_haubenreiser

This is a project dedicated to the classification of emotional speech and was created in class with Prof. Dr. Burkhardt at Technische Universität Berlin.

Individuelle Sprechstile

This is a project dedicated to the classification of emotional speech and was created in class with Prof. Dr. Burkhardt at Technische Universität Berlin. After choosing a short speech of any person, the aim of the project was to analyze the voice recording by taking a closer look at target acoustic features and classify the speech accordingly.

The project uses Jupyter, an open-source web application that allows you to create and share documents that contain live code that can be used for statistical modeling and data visualization.

Learning Objectives

• Introduction to Python and Juypter Notebook
• Classification of Emotional Speech
• Acoustic Analysis through Extraction and Analysis of Expert and Brute-Force Features
• Visualization of Statistical Findings

Preparation

In order to run this project, some preliminary steps are necessary:

• Create a virtual environment for the project and collect the necessary imports. If a certain plug-in is missing, it is recommended to install it via Pip in the terminal, so that the import into Jupyter Notebook can run smoothly. You will definitively need: Pandas, os, Matplotlib, seaborn, glob, NumPy, Parselmouth and SciPy.
• Prepare the audio file for the analysis. The format should be: wav, 16kHz, 16bit, PCM (possible tool: SoX/Audacity).
• Segment and annotate your data according to the dimension valence on a 10-level Likert scale (possible tool: Audacity, Speechalyzer).

Documentation

After configuring a python environment with Jupyter notebook the audio is analyzed by using Parselmouth, general sound characteristics are extracted before diving in deeper. Necessary information on the single steps are included in the notebook.

Extracting Acoustic Features

The projects firstly analyses the unsegmented sound with the tool Parselmouth.
This project further uses Uwe Reichelts Script for MLDs and LLDs via OpenSmile, MyVoiceAnalysis, AutomaticSpeechRecognition as well as a t-SNE plot to perform a visualized comparative analysis between different analyzed sounds. It should offer a comprehensible code to perform your analyses. Nevertheless, you might need to adapt the scripts to fit your needs.

The overall analysis includes:

• Formants
• Syllables, Pauses, Speech and Arcticulation Rate, f0 characteristics
• LLDs
• MLDs
• Functionals
• Speech Recognition

Visualization

The project includes different approaches to visualize the data using the tool seaborn, e.g. scatter plots, box/violin plots or cluster plots.

Credits

• Speechalyzer
• Feinberg PraatScripts
• OpenSmile

Author: @lou-ukw