Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion

• URL: http://arxiv.org/abs/2208.08757v1
• Date: Thu, 18 Aug 2022 10:36:27 GMT
• Title: Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
• Authors: SiCheng Yang, Methawee Tantrawenith, Haolin Zhuang, Zhiyong Wu, Aolan Sun, Jianzong Wang, ning cheng, Huaizhen Tang, Xintao Zhao, Jie Wang and Helen Meng
• Abstract summary: One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic. We employ random resampling for pitch and content encoder and use the variational contrastive log-ratio upper bound of mutual information to disentangle speech components. Experiments on the VCTK dataset show the model achieves state-of-the-art performance for one-shot VC in terms of naturalness and intellgibility.
• Score: 42.43123253495082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic. Existing works generally disentangle timbre, while information about pitch, rhythm and content is still mixed together. To perform one-shot VC effectively with further disentangling these speech components, we employ random resampling for pitch and content encoder and use the variational contrastive log-ratio upper bound of mutual information and gradient reversal layer based adversarial mutual information learning to ensure the different parts of the latent space containing only the desired disentangled representation during training. Experiments on the VCTK dataset show the model achieves state-of-the-art performance for one-shot VC in terms of naturalness and intellgibility. In addition, we can transfer characteristics of one-shot VC on timbre, pitch and rhythm separately by speech representation disentanglement. Our code, pre-trained models and demo are available at https://im1eon.github.io/IS2022-SRDVC/.

Related papers

• SelfVC: Voice Conversion With Iterative Refinement using Self Transformations [42.97689861071184]
  SelfVC is a training strategy to improve a voice conversion model with self-synthesized examples. We develop techniques to derive prosodic information from the audio signal and SSL representations to train predictive submodules in the synthesis model. Our framework is trained without any text and achieves state-of-the-art results in zero-shot voice conversion on metrics evaluating naturalness, speaker similarity, and intelligibility of synthesized audio.
  arXiv  Detail & Related papers  (2023-10-14T19:51:17Z)
• Towards General-Purpose Text-Instruction-Guided Voice Conversion [84.78206348045428]
  This paper introduces a novel voice conversion model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice" The proposed VC model is a neural language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech.
  arXiv  Detail & Related papers  (2023-09-25T17:52:09Z)
• Learning Speech Representation From Contrastive Token-Acoustic Pretraining [57.08426714676043]
  We propose "Contrastive Token-Acoustic Pretraining (CTAP)", which uses two encoders to bring phoneme and speech into a joint multimodal space. The proposed CTAP model is trained on 210k speech and phoneme pairs, achieving minimally-supervised TTS, VC, and ASR.
  arXiv  Detail & Related papers  (2023-09-01T12:35:43Z)
• Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units [27.619740864818453]
  We introduce DISSC, a novel, lightweight method that converts the rhythm, pitch contour and timbre of a recording to a target speaker in a textless manner. The proposed approach uses a pretrained, self-supervised model for encoding speech to discrete units, which makes it simple, effective, and fast to train.
  arXiv  Detail & Related papers  (2022-12-19T18:53:04Z)
• Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion [34.139871476234205]
  We investigate zero-shot voice conversion from a novel perspective of self-supervised disentangled speech representation learning. A zero-shot voice conversion is performed by feeding an arbitrary speaker embedding and content embeddings to a sequential variational autoencoder (VAE) decoder. On TIMIT and VCTK datasets, we achieve state-of-the-art performance on both objective evaluation, i.e., speaker verification (SV) on speaker embedding and content embedding, and subjective evaluation, i.e. voice naturalness and similarity, and remains to be robust even with noisy source/target utterances.
  arXiv  Detail & Related papers  (2022-03-30T23:03:19Z)
• Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features [24.182732872327183]
  Unsampling Zero-Shot Voice Conversion (VC) aims to modify the speaker characteristic of an utterance to match an unseen target speaker. We show that high-quality audio samples can be achieved by using a length resupervised decoder.
  arXiv  Detail & Related papers  (2021-12-08T17:27:39Z)
• VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion [54.29557210925752]
  One-shot voice conversion can be effectively achieved by speech representation disentanglement. We employ vector quantization (VQ) for content encoding and introduce mutual information (MI) as the correlation metric during training. Experimental results reflect the superiority of the proposed method in learning effective disentangled speech representations.
  arXiv  Detail & Related papers  (2021-06-18T13:50:38Z)
• Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments [76.98764900754111]
  Voice Conversion (VC) is a technique that aims to transform the non-linguistic information of a source utterance to change the perceived identity of the speaker. We propose Voicy, a new VC framework particularly tailored for noisy speech. Our method, which is inspired by the de-noising auto-encoders framework, is comprised of four encoders (speaker, content, phonetic and acoustic-ASR) and one decoder.
  arXiv  Detail & Related papers  (2021-06-16T15:47:06Z)
• Many-to-Many Voice Transformer Network [55.17770019619078]
  This paper proposes a voice conversion (VC) method based on a sequence-to-sequence (S2S) learning framework. It enables simultaneous conversion of the voice characteristics, pitch contour, and duration of input speech.
  arXiv  Detail & Related papers  (2020-05-18T04:02:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.