6 research outputs found
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion
This paper presents a refinement framework of WaveNet vocoders for
variational autoencoder (VAE) based voice conversion (VC), which reduces the
quality distortion caused by the mismatch between the training data and testing
data. Conventional WaveNet vocoders are trained with natural acoustic features
but conditioned on the converted features in the conversion stage for VC, and
such a mismatch often causes significant quality and similarity degradation. In
this work, we take advantage of the particular structure of VAEs to refine
WaveNet vocoders with the self-reconstructed features generated by VAE, which
are of similar characteristics with the converted features while having the
same temporal structure with the target natural features. We analyze these
features and show that the self-reconstructed features are similar to the
converted features. Objective and subjective experimental results demonstrate
the effectiveness of our proposed framework.Comment: 5 pages, 7 figures, 1 table. Accepted to EUSIPCO 201
L2-ARCTIC: A Non-Native English Speech Corpus
In this paper, we introduce L2-ARCTIC, a speech corpus of non-native English that is intended for research in voice conversion, accent conversion, and mispronunciation detection. This initial release includes recordings from ten non-native speakers of English whose first languages (L1s) are Hindi, Korean, Mandarin, Spanish, and Arabic, each L1 containing recordings from one male and one female speaker. Each speaker recorded approximately one hour of read speech from the Carnegie Mellon University ARCTIC prompts, from which we generated orthographic and forced-aligned phonetic transcriptions. In addition, we manually annotated 150 utterances per speaker to identify three types of mispronunciation errors: substitutions, deletions, and additions, making it a valuable resource not only for research in voice conversion and accent conversion but also in computer-assisted pronunciation training. The corpus is publicly accessible at https://psi.engr.tamu.edu/l2-arctic-corpus/