mlsanityData to 14 Oct 2026

MetaSpeech: Speech Effects Switch Along with Environment for Metaverse

arXiv 2210.13811 ↗First posted 25 Oct 2022cs.SD cs.CL eess.AScited by 0

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Abstract (arXiv)

Metaverse expands the physical world to a new dimension, and the physical environment and Metaverse environment can be directly connected and entered. Voice is an indispensable communication medium in the real world and Metaverse. Fusion of the voice with environment effects is important for user immersion in Metaverse. In this paper, we proposed using the voice conversion based method for the conversion of target environment effect speech. The proposed method was named MetaSpeech, which introduces an environment effect module containing an effect extractor to extract the environment information and an effect encoder to encode the environment effect condition, in which gradient reversal layer was used for adversarial training to keep the speech content and speaker information while disentangling the environmental effects. From the experiment results on the public dataset of LJSpeech with four environment effects, the proposed model could complete the specific environment effect conversion and outperforms the baseline methods from the voice conversion task.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 37 references that are in the map. First posted on arXiv 25 Oct 2022.

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References · 25

  1. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 717
  2. 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 192
  3. 2020Unsupervised Speech Decomposition via Triple Information Bottleneckcited by 88
  4. 2021All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agendacited by 67
  5. 2019CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversioncited by 46
  6. 2016Voice Conversion from Non-parallel Corpora Using Variational Auto-encodercited by 45
  7. 2020AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalizationcited by 44
  8. 2021FAST-RIR: Fast neural diffuse room impulse response generatorcited by 35
  9. 2017Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networkscited by 31
  10. 2020CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversioncited by 25
  11. 2022AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learningcited by 24
  12. 2021MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Framescited by 18
13 more
  1. 2021Metaverse for Social Good: A University Campus Prototypecited by 17
  2. 2022Realizing the Metaverse with Edge Intelligence: A Match Made in Heavencited by 17
  3. 2021TS-RIR: Translated synthetic room impulse responses for speech augmentationcited by 12
  4. 2021Differentiable Signal Processing With Black-Box Audio Effectscited by 10
  5. 2022nnSpeech: Speaker-Guided Conditional Variational Autoencoder for Zero-shot Multi-speaker Text-to-Speechcited by 10
  6. 2018ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencodercited by 9
  7. 2022DRVC: A Framework of Any-to-Any Voice Conversion with Self-Supervised Learningcited by 9
  8. 2021Non-autoregressive sequence-to-sequence voice conversioncited by 7
  9. 2021Global Rhythm Style Transfer Without Text Transcriptionscited by 6
  10. 2020Towards Low-Resource StarGAN Voice Conversion using Weight Adaptive Instance Normalizationcited by 4
  11. 2022TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Trainingcited by 4
  12. 2021Singer Identification Using Deep Timbre Feature Learning with KNN-Netcited by 3
  13. 2020Exploring Quality and Generalizability in Parameterized Neural Audio Effectscited by 1

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