Search Results for author:
Found 11 papers, 0 papers with code
NPU-NTU System for Voice Privacy 2024 Challenge
Our system employs a disentangled neural codec architecture and a serial disentanglement strategy to gradually disentangle the global speaker identity and time-variant linguistic content and paralinguistic information.
Drop the beat! Freestyler for Accompaniment Conditioned Rapping Voice Generation
Rap, a prominent genre of vocal performance, remains underexplored in vocal generation.
MUSA: Multi-lingual Speaker Anonymization via Serial Disentanglement
Meanwhile, we propose a straightforward anonymization strategy that employs empty embedding with zero values to simulate the speaker identity concealment process, eliminating the need for conversion to a pseudo-speaker identity and thereby reducing the complexity of speaker anonymization process.
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion
With speaker-independent semantic tokens to guide the training of the content encoder, the dependency on ASR is removed and the model can operate under extremely small chunks, with cascading errors eliminated.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Distinctive and Natural Speaker Anonymization via Singular Value Transformation-assisted Matrix
To address these issues and especially generate more natural and distinctive anonymized speech, we propose a novel speaker anonymization approach that models a matrix related to speaker identity and transforms it into an anonymized singular value transformation-assisted matrix to conceal the original speaker identity.
VITS-Based Singing Voice Conversion Leveraging Whisper and multi-scale F0 Modeling
This paper introduces the T23 team's system submitted to the Singing Voice Conversion Challenge 2023.
DualVC 2: Dynamic Masked Convolution for Unified Streaming and Non-Streaming Voice Conversion
Third, the model is unable to effectively address the noise in the unvoiced segments, lowering the sound quality.
PromptVC: Flexible Stylistic Voice Conversion in Latent Space Driven by Natural Language Prompts
In this study, we propose PromptVC, a novel style voice conversion approach that employs a latent diffusion model to generate a style vector driven by natural language prompts.
DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding
Voice conversion is an increasingly popular technology, and the growing number of real-time applications requires models with streaming conversion capabilities.
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
We further fuse the linguistic and para-linguistic features through an attention mechanism, where speaker-dependent prosody features are adopted as the attention query, which result from a prosody encoder with target speaker embedding and normalized pitch and energy of source speech as input.
Preserving background sound in noise-robust voice conversion via multi-task learning
Background sound is an informative form of art that is helpful in providing a more immersive experience in real-application voice conversion (VC) scenarios.
