Extended Abstract: Neural-Network Based Auditory Feedback Modulation in Real-Time

IEEE International Professional Communication Conference (ProComm), pp. 337–339

Abstract

An impaired voice quality (i.e., hoarseness) is a key symptom of voice disorders, including functional dysphonia. Auditory feedback modulation (AFM) of voice quality, in which patients' existing hoarseness is intensified through headphones, could potentially offer a noninvasive treatment approach by encouraging compensatory vocal adjustments that improve voice quality. This study aims to artificially induce hoarseness using neural networks (NNs) for the modification of the cepstrum. Since cepstral parameters like cepstral peak prominence (CPP), jitter, and shimmer reflect voice quality, we propose that targeted cepstral modifications could be used to simulate hoarseness. A multilayer perceptron (MLP), as a very simple form of a neural network, ensures real-time processing, paving the way for using artificial intelligence in future AFM applications in speech therapy and training.

Authors 5

1. Henning Eikens

   Christian-Albrechts-Universität zu Kiel

   Affiliation as printed

   Kiel University

2. Karolin Krüger

   Christian-Albrechts-Universität zu Kiel

   Affiliation as printed

   Kiel University

3. Finn Röhrdanz

   Christian-Albrechts-Universität zu Kiel

   Affiliation as printed

   Kiel University

4. Gerhard Uwe Schmidt

   Christian-Albrechts-Universität zu Kiel

   Affiliation as printed

   Kiel University

5. Isabel Schiller Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University