How To Convert Many To One Voice Without Parallel Data Training

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PHONETIC POSTERIORGRAMS FOR MANY-TO-ONE VOICE CONVERSION WITHOUT PARALLEL DATA TRAINING Lifa Sun, Kun Li, Hao Wang, Shiyin Kang and Helen Meng Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Hong Kong SAR, China flfsun,kli,hwang,sykang,[email protected]

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To convert arbitrary source speech, we obtain its PPGs from the same SI-ASR and feed them into the trained DBLSTM for generating converted speech. Our approach has two main advantages: 1) no parallel training data is required; 2) a trained model can be applied to any other source speaker for a fixed target speaker (i.e., many-to-one conversion). Experiments …

Author: Lifa Sun, Kun Li, Hao Wang, Shiyin Kang, Helen Meng
Publish Year: 2016

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1. What if you could imitate a famous celebrity's voice or sing like a famous singer?This project started with a goal to convert someone's voice to a specific target voice.So called, it's voice style transfer.We worked on this project that aims to convert someone's voice to a famous English actress Kate Winslet'svoice.We implemented a deep neural networks to achieve that and more than 2 hours of audio book sentences read by Kate Winslet are used as a dataset.

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In non-parallel data voice conversion with the provided text, a simple method is to produce parallel audios from the text-to-speech model. As shown in Fig. 1, given the collected data of two speakers with different text groups, the text-to-speech model is first trained with the data of speaker A.

Author: Bo Chen, Zhihang Xu, Kai Yu
Publish Year: 2022

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Phonetic posteriorgrams for many-to-one voice conversion without parallel data training in Proc, IEEE International Conference on Multimedia and Expo ( 2016 ) , pp. 1 - …

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Our approach was to be able to perform voice transfer on non-parallel data. This means that the style voice could be saying something completely different than the content voice. We hoped to design a model that could learn a latent space encoding higher dimensional information in audio and would be able to generalize the style voice without necessarily being …

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This is a many-to-one voice conversion system.The main significance of this work is that we could generate a target speaker's utterances without parallel data like , or , but only waveforms of the target speaker.(To make these parallel datasets needs a lot of effort.)All we need in this project is a number of waveforms of the target speaker's utterances and only a …

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and the second one from the eigenvoice to the target speaker. Thus, even if these approaches do not require parallel training data from the source and the target, they do require parallel data of the reference speakers (for an opposite case, see [15]). As opposed to the eigen-voice approach, our method does not require any parallel data, any

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We present an unsupervised non-parallel many-to-many voice conversion (VC) method using a generative adversarial network (GAN) called StarGAN v2. Using a …

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It is straightforward to apply EVC to many-to-one VC by switching the source and the target as described in [7]. 2.1. Eigenvoice Gaussian mixture model (EV -GMM) We emplo y 2D -dimensional acoustic

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One very interesting choice that one makes when creating such a system is the selection of which voice to use for the generated audio. Should it be a man or a woman? A loud voice or a soft one? This used to present a restriction when doing TTS with Deep Learning. You’d have to collect a dataset of text-speech pairs. The set of speakers who recorded that speech …

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PDF On Jul 1, 2016, Lifa Sun and others published Phonetic posteriorgrams for many-to-one voice conversion without parallel data training Find, read and …

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first, autoencoder doesn’t require parallel training data during encoding and decoding; second, the encoder-decoder architecture allows for effective control of singer identity and singing prosody, that makes many-to-many conversion easier than other non-parallel generative models, such as cycle-consistent generative adversarial network …

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Nonetheless, without sufficient data, seq2seq VC models can suffer from unstable training and mispronunciation problems in the converted speech, thus far from practical. To tackle these shortcomings, we propose to transfer knowledge from other speech processing tasks where large-scale corpora are easily available, typically text-to-speech (TTS) and automatic …

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There are many ways to convert voice recordings to text. In this post, we list the top five ways you can do so. 1. Use Online Dictation Software. Online dictation services are among the most common methods available for converting audio files into text. Online dictation software can transcribe real-time audio. Simply navigate to the online platform, put in your …

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Frequently Asked Questions

Is there a non parallel method for Voice Conversion??

Various approaches have been proposed [2] for voice conversion. As parallel data [1] is expensive to collect, non-parallel methods [3,4, 5] have received significant attention. Among them, phonetic posteriorgram (PPG) [5] based method is one of the most popular implementations. ... ...

Can a generative adversarial network convert many to many voice conversions??

nii-yamagishilab/project-NN-Pytorch… This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.

What is the difference between CVAE and voice conversion without parallel data??

Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-target scenario in which a single model is trained to convert the input voice to many different speakers. On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN.

Is there a many to one voice conversion system??

This is a many-to-one voice conversion system. The main significance of this work is that we could generate a target speaker's utterances without parallel data like <source's wav, target's wav>, <wav, text> or <wav, phone>, but only waveforms of the target speaker. (To make these parallel datasets needs a lot of effort.)

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