Reclamation-based-Voice(talk)-Conversion
- 1. Reclamation-based-Voice- Conversion(RVC) GURUMURTHY.V(421122102050) DHIVYAKUMARAN.K(421122102037) GOKULRAJ.R(421122102044) PRESENTED BY Under The Guidance of MRS.K.KALAISELVI/AP/CSE
- 2. PROBLEM STATEMENT •Naturalness: Maintaining the naturalness of the source speaker's voice while converting it to the target speaker is challenging. Artifacts and distortions can occur. •Data Quality: Noise, background interference, and inconsistent recording conditions can negatively impact the performance of the model. •Accurate Retrieval: The quality of the converted speech depends on the accuracy of the retrieved target speaker utterances. Ineffective retrieval can result in mismatched voice characteristics.
- 3. PROBLEM STATEMENT DIAGRAM • The quality of the data significantly impacts the model's performance. • Noise, background interference, and inconsistent recording conditions can degrade . • The development of timbre conversion and imitation is not perfect.
- 4. • Voice conversion models require a vast amount of data to learn intricate voice characteristics. • Collecting and processing such a large dataset can be time-consuming and expensive.
- 5. PROPOSED SYSTEM •A powerful speech encoder is trained on a large dataset to extract high-level acoustic features that capture speaker identity and linguistic content. • Given a target speaker's voice, a set of representative speech segments is extracted and encoded into the same feature space. •For a given input speech, the encoder generates its corresponding feature representation.
- 6. EXISTING SYSTEM S. NO TITLE AUTHOR YEAR PUBLISHED DRAWBACK 1. Voice Conversion from a Single Speaker Example Jun Wu, Yi Li, and Yiheng Liu 2015 Requires a large amount of training data for each speaker. 2. A Waveform Generation Model for High-Quality Speech Synthesis Tomoki Kaneko, Kazuhiro Takahashi, and Shinji Nakamura 2018 Can produce artifacts in the synthesized speech, especially for unseen speakers. 3. A Generative Adversarial Network Approach to Text-to- Speech Synthesis Zheng Wang, Jonathan Sotelo, Haoyu Wu, Gregor Kurz 2017 The generated speech quality can be sensitive to the quality of the text-to- speech (TTS) model used.
- 7. S. NO TITLE AUTHOR YEAR PUBLISHED DRAWBACK 4. A Generative Model for Raw Audio Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu 2016 Requires a significant amount of computational resources for training and inference. 5. A Flow-based Generative Model for Text-to-Speech Synthesis Jongwook Kim, Hyunjun Kim, and Ho- Sang Lee 2020 Can struggle with preserving the naturalness and expressiveness of the original voice.
- 8. THANK YOU