The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition
Luke Prananta (Student TU Delft)
Bence Mark Halpern (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, TU Delft - Electrical Engineering, Mathematics and Computer Science, Universiteit van Amsterdam)
Siyuan Feng (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Odette Scharenborg (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
In this paper, we investigate several existing and a new state-of-the-art generative adversarial network-based (GAN) voice conversion method for enhancing dysarthric speech for improved dysarthric speech recognition. We compare key components of existing methods as part of a rigorous ablation study to find the most effective solution to improve dysarthric speech recognition. We find that straightforward signal processing methods such as stationary noise removal and vocoder-based time stretching lead to dysarthric speech recognition results comparable to those obtained when using state-of-the-art GAN-based voice conversion methods as measured using a phoneme recognition task. Additionally, our proposed solution of a combination of MaskCycleGAN-VC and time stretching is able to improve the phoneme recognition results for certain dysarthric speakers compared to our time stretched baseline.