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Pathological voice adaptation with autoencoder-based voice conversion

Authors :
Illa, Marc
Halpern, Bence Mark
van Son, Rob
Moro-Velazquez, Laureano
Scharenborg, Odette
Publication Year :
2021

Abstract

In this paper, we propose a new approach to pathological speech synthesis. Instead of using healthy speech as a source, we customise an existing pathological speech sample to a new speaker's voice characteristics. This approach alleviates the evaluation problem one normally has when converting typical speech to pathological speech, as in our approach, the voice conversion (VC) model does not need to be optimised for speech degradation but only for the speaker change. This change in the optimisation ensures that any degradation found in naturalness is due to the conversion process and not due to the model exaggerating characteristics of a speech pathology. To show a proof of concept of this method, we convert dysarthric speech using the UASpeech database and an autoencoder-based VC technique. Subjective evaluation results show reasonable naturalness for high intelligibility dysarthric speakers, though lower intelligibility seems to introduce a marginal degradation in naturalness scores for mid and low intelligibility speakers compared to ground truth. Conversion of speaker characteristics for low and high intelligibility speakers is successful, but not for mid. Whether the differences in the results for the different intelligibility levels is due to the intelligibility levels or due to the speakers needs to be further investigated.<br />Comment: 6 pages, 3 figures. Accepted to the 11th ISCA Speech Synthesis Workshop (2021)

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2106.08427
Document Type :
Working Paper