Proceedings ArticleDOI
Can voice conversion be used to reduce non-native accents?
Sandesh Aryal,Ricardo Gutierrez-Osuna +1 more
- pp 7879-7883
TLDR
A modification of the conventional training process for VC that allows it to perform as an AC transform is proposed, which pair source and target vectors based not on their ordering within a parallel corpus, but based on their linguistic similarity.Abstract:
Voice-conversion (VC) techniques aim to transform utterances from a source speaker to sound as if they had been produced by a target speaker. This includes not only organic properties (i.e., voice quality) but also linguistic cues (i.e., regional accents) of the target speaker. For this reason, VC is generally ill-suited for accent-conversion (AC) purposes, where the goal is to capture the voice quality of the target speaker but the regional accent of the source speaker. In this paper, we propose a modification of the conventional training process for VC that allows it to perform as an AC transform. The approach consists of pairing source and target vectors based not on their ordering within a parallel corpus, as is commonly done in VC, but based on their linguistic similarity. We validate the AC approach on a corpus containing native-accented and Spanish-accented utterances, and compare it against conventional VC through a series of perceptual listening tests. We also analyze the extent to which phonological differences between the two languages (Spanish and American English) help predict the relative performance of the two methods.read more
Citations
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Proceedings ArticleDOI
L2-ARCTIC: A Non-Native English Speech Corpus
Guanlong Zhao,Sinem Sonsaat,Alif Silpachai,Ivana Lucic,Evgeny Chukharev-Hudilainen,John M. Levis,Ricardo Gutierrez-Osuna +6 more
TL;DR: L2-ARCTIC is introduced, a speech corpus of non-native English that is intended for research in voice conversion, accent conversion, and mispronunciation detection, and is publicly accessible at https://psi.tamu.edu/l2-arctic-corpus/.
Proceedings ArticleDOI
Accent Conversion Using Phonetic Posteriorgrams
Guanlong Zhao,Sinem Sonsaat,John M. Levis,Evgeny Chukharev-Hudilainen,Ricardo Gutierrez-Osuna +4 more
TL;DR: An approach that matches frames between the two speakers based on their phonetic (rather than acoustic) similarity and improves the ratings of acoustic quality and native accent while retaining the voice identity of the non-native speaker is proposed.
Proceedings ArticleDOI
Foreign Accent Conversion by Synthesizing Speech from Phonetic Posteriorgrams.
TL;DR: This work presents a framework for FAC that eliminates the need for conventional vocoders and therefore the need to use the native speaker’s excitation, and produces speech that sounds more clear, natural, and similar to the non-native speaker compared with a baseline system.
Posted Content
Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities
TL;DR: In this article, a sequence-to-sequence learning of context posterior probabilities is proposed to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities, where the source posterior probabilities are directly used for predicting target speech parameters.
Journal ArticleDOI
NAUTILUS: A Versatile Voice Cloning System
Hieu-Thi Luong,Junichi Yamagishi +1 more
TL;DR: In this paper, a novel speech synthesis system, called NAUTILUS, is proposed that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker.
References
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