Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: The Sequence-to-Sequence Baseline for the Voice Conversion Challenge
2020: Cascading ASR and TTS
summary: This paper presents the sequence-to-sequence (seq2seq) baseline system for
the voice conversion challenge (VCC) 2020. We consider a naive approach for
voice conversion (VC), which is to first transcribe the input speech with an
automatic speech recognition (ASR) model, followed using the transcriptions to
generate the voice of the target with a text-to-speech (TTS) model. We revisit
this method under a sequence-to-sequence (seq2seq) framework by utilizing
ESPnet, an open-source end-to-end speech processing toolkit, and the many
well-configured pretrained models provided by the community. Official
evaluation results show that our system comes out top among the participating
systems in terms of conversion similarity, demonstrating the promising ability
of seq2seq models to convert speaker identity. The implementation is made
open-source at: https://github.com/espnet/espnet/tree/master/egs/vcc20.
Thunk you very much for contribution!
Your judgement is refrected in arXivSearches.json, and is going to be used for VCLab's activity.
Thunk you so much.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
summary: This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice conversion challenge (VCC) 2020. We consider a naive approach for voice conversion (VC), which is to first transcribe the input speech with an automatic speech recognition (ASR) model, followed using the transcriptions to generate the voice of the target with a text-to-speech (TTS) model. We revisit this method under a sequence-to-sequence (seq2seq) framework by utilizing ESPnet, an open-source end-to-end speech processing toolkit, and the many well-configured pretrained models provided by the community. Official evaluation results show that our system comes out top among the participating systems in terms of conversion similarity, demonstrating the promising ability of seq2seq models to convert speaker identity. The implementation is made open-source at: https://github.com/espnet/espnet/tree/master/egs/vcc20.
id: http://arxiv.org/abs/2010.02434v1
judge
Write [vclab::confirmed] or [vclab::excluded] in comment.