Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion
Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion
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.
- University of Florida United States
- MES College of Engineering India
- Technische Hochschule Ingolstadt Germany
- Technische Hochschule Ingolstadt Germany
- Florida State University United States
FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Computation and Language, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Computation and Language (cs.CL), Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Computation and Language, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Computation and Language (cs.CL), Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
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