Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion

• URL: http://arxiv.org/abs/2005.07025v3
• Date: Tue, 13 Oct 2020 06:07:16 GMT
• Title: Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion
• Authors: Kun Zhou, Berrak Sisman, Mingyang Zhang and Haizhou Li
• Abstract summary: Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. We propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data. Experiments show that the proposed speaker-independent framework achieves competitive results for both seen and unseen speakers.
• Score: 83.14445041096523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotional voice conversion are mostly carried out under the assumption that emotion is speaker-dependent. We consider that there is a common code between speakers for emotional expression in a spoken language, therefore, a speaker-independent mapping between emotional states is possible. In this paper, we propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data. We propose a VAW-GAN based encoder-decoder structure to learn the spectrum and prosody mapping. We perform prosody conversion by using continuous wavelet transform (CWT) to model the temporal dependencies. We also investigate the use of F0 as an additional input to the decoder to improve emotion conversion performance. Experiments show that the proposed speaker-independent framework achieves competitive results for both seen and unseen speakers.

Related papers

• Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion [81.1492897350032]
  Emotional Voice Conversion aims to manipulate a speech according to a given emotion while preserving non-emotion components. We propose an Attention-based Interactive diseNtangling Network (AINN) that leverages instance-wise emotional knowledge for voice conversion.
  arXiv  Detail & Related papers  (2023-12-29T08:06:45Z)
• In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis [15.16865739526702]
  We introduce a methodology that uses self-supervised networks to disentangle the lexical, speaker, and emotional content of the utterance. We then use a HiFiGAN vocoder to resynthesise the disentangled representations to a speech signal of the targeted emotion. Results reveal that the proposed approach is aptly conditioned on the emotional content of input speech and is capable of synthesising natural-sounding speech for a target emotion.
  arXiv  Detail & Related papers  (2023-06-02T21:02:51Z)
• Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing [9.354935229153787]
  We tackle the problem of converting the emotion of speakers whose only neutral data are present during the time of training and testing. We propose a Virtual Domain Pairing (VDP) training strategy, which virtually incorporates the speaker-emotion pairs that are not present in the real data. We evaluate the proposed method using a Hindi emotional database.
  arXiv  Detail & Related papers  (2023-02-21T09:06:52Z)
• Textless Speech Emotion Conversion using Decomposed and Discrete Representations [49.55101900501656]
  We decompose speech into discrete and disentangled learned representations, consisting of content units, F0, speaker, and emotion. First, we modify the speech content by translating the content units to a target emotion, and then predict the prosodic features based on these units. Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder.
  arXiv  Detail & Related papers  (2021-11-14T18:16:42Z)
• Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networks [14.55242023708204]
  We propose a novel Source-Filter-based Emotional VC model (SFEVC) to achieve proper filtering of speaker-independent emotion features. Our SFEVC model consists of multi-channel encoders, emotion separate encoders, and one decoder.
  arXiv  Detail & Related papers  (2021-10-04T03:14:48Z)
• Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Sequence-to-Sequence Training [91.95855310211176]
  Emotional voice conversion aims to change the emotional state of an utterance while preserving the linguistic content and speaker identity. We propose a novel 2-stage training strategy for sequence-to-sequence emotional voice conversion with a limited amount of emotional speech data. The proposed framework can perform both spectrum and prosody conversion and achieves significant improvement over the state-of-the-art baselines in both objective and subjective evaluation.
  arXiv  Detail & Related papers  (2021-03-31T04:56:14Z)
• VAW-GAN for Disentanglement and Recomposition of Emotional Elements in Speech [91.92456020841438]
  We study the disentanglement and recomposition of emotional elements in speech through variational autoencoding Wasserstein generative adversarial network (VAW-GAN) We propose a speaker-dependent EVC framework that includes two VAW-GAN pipelines, one for spectrum conversion, and another for prosody conversion. Experiments validate the effectiveness of our proposed method in both objective and subjective evaluations.
  arXiv  Detail & Related papers  (2020-11-03T08:49:33Z)
• Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset [84.53659233967225]
  Emotional voice conversion aims to transform emotional prosody in speech while preserving the linguistic content and speaker identity. We propose a novel framework based on variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) We show that the proposed framework achieves remarkable performance by consistently outperforming the baseline framework.
  arXiv  Detail & Related papers  (2020-10-28T07:16:18Z)

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.