Exemplar-Based Methods for Mandarin Electrolaryngeal Speech Voice Conversion

Electrolarynx (EL) can restore speech to patients without vocal cords, but the resulting speech often lacks naturalness and intelligibility. Inspired by recent advances in exemplar-based methods for speaker voice conversion (VC), this study explores their application to electrolaryngeal speech voice conversion (ELVC). As a baseline, we apply the k-nearest neighbors VC (KNN-VC) method with self-supervised learning (SSL) representations to ELVC. To circumvent the SSL feature mismatch between EL speech and natural speech when applying KNN-VC to ELVC, we propose two meth-ods under the parallel VC setting: the parallel KNN-ELVC method and the parallel locally linear embedding (LLE)-based ELVC method. Experimental results demonstrate that while directly applying KNN-VC to ELVC improves the naturalness of EL speech, our proposed methods, especially LLE-ELVC, significantly improve both naturalness and in-telligibility. Furthermore, LLE-ELVC is comparable to our previous deep-learning-based ELVC method, further demon-strating the potential of exemplar-based methods in ELVC tasks.