Analysis of Acoustic Parameters of Vowels for Detecting Voice Changes in Long-Term Voice Recordings
- Status
- In work
- Type
- Master Project
- Announcement date
- 07 Mar 2021
- Student
- Simon Lohrmann
- Mentors
- Research Areas
Abstract:
Extensive lectures influence a speaker’s voice quality after a certain period of time. In this thesis the voice changes in vowels of individual speakers should be analyzed by generating and evaluating a suitable feature space in order to enable a voice quality characterization in real-time. Vocal fatigue related features must be found within a literature research (feature extraction) and the extracted parameters should be analyzed with statistical methods to determine a beneficial feature set for a binary classification task (feature selection/ranking). Finally, the data should be classified with the use of state-of-the-art machine learning algorithms in the sense of supervised learning.
Tasks:
- organisation and provision of useful audio recordings (ca. 2h) with the help of a mobile recording system (cooperation with the company audEERING)
- literature research (vocal fatigue, vocal loading, voice dosimetry, voice monitoring, …) in order to extract a suitable feature set (e.g. with openSMILE)
- analyzing the feature space with statistical methods
- binary classification with the help of machine learning algorithms