'Voice Conversion' paper candidate 2311.14836

[github-actions[bot]]

Please check whether this paper is about 'Voice Conversion' or not.

article info.

• title: Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion

• summary: This paper proposes two innovative methodologies to construct customized Common Voice datasets for low-resource languages like Hindi. The first methodology leverages Bark, a transformer-based text-to-audio model developed by Suno, and incorporates Meta's enCodec and a pre-trained HuBert model to enhance Bark's performance. The second methodology employs Retrieval-Based Voice Conversion (RVC) and uses the Ozen toolkit for data preparation. Both methodologies contribute to the advancement of ASR technology and offer valuable insights into addressing the challenges of constructing customized Common Voice datasets for under-resourced languages. Furthermore, they provide a pathway to achieving high-quality, personalized voice generation for a range of applications.

• id: http://arxiv.org/abs/2311.14836v1

  judge

  Write [vclab::confirmed] or [vclab::excluded] in comment.

[tarepan]

[vclab::confirmed]

[github-actions[bot]]

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.