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Development of a deep learning-based phoneme recognizer for noisy speech

Type: Project

Status: running

Supervisors: Hendrik Schröter

For speech intelligibility, consonants have a fundamental importance. Thus, the assumption can be made that automatic phoneme and especially consonant recognition correlates well with human speech intelligibility.
In noisy environments however, speech and especially consonants may be degraded.

In this project, we want to study the effect of noise on speech intelligibility. Therefore, we train a neural network to recognize phoneme based on the TIMIT dataset. We will add diffent noise types and noise levels to the speech signal and study the effect on the recognition rate.

This project requires no preliminary knowledge in deep learning, although may be beneficial. Basic signal processing concepts like sampling theorem and FFT should be present.