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NTU-NPU System for Voice Privacy 2024 Challenge
In this work, we describe our submissions for the Voice Privacy Challenge 2024.
Takin-VC: Zero-shot Voice Conversion via Jointly Hybrid Content and Memory-Augmented Context-Aware Timbre Modeling
Zero-shot voice conversion (VC) aims to transform the source speaker timbre into an arbitrary unseen one without altering the original speech content. While recent advancements in zero-shot VC methods have shown remarkable progress, there still remains considerable potential for improvement in terms of improving speaker similarity and speech naturalness. In this paper, we propose Takin-VC, a novel zero-shot VC framework based on jointly hybrid content and memory-augmented context-aware timbre modeling to tackle this challenge.
Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models
In this report, we introduce Takin AudioLLM, a series of techniques and models, mainly including Takin TTS, Takin VC, and Takin Morphing, specifically designed for audiobook production.
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)
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Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction
To enhance the distinctiveness of the reconstructed features by each decoder, we incorporate contrastive learning and an auxiliary classifier to further constrain the reconstructed feature.
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.
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.
MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition
Despite notable progress, speech emotion recognition (SER) remains challenging due to the intricate and ambiguous nature of speech emotion, particularly in wild world.
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition
Contrastive cross-modality pretraining has recently exhibited impressive success in diverse fields, whereas there is limited research on their merits in speech emotion recognition (SER).
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.
Distinguishable Speaker Anonymization based on Formant and Fundamental Frequency Scaling
By directly scaling the formant and F0, the speaker distinguishability degradation of the anonymized speech caused by the introduction of other speakers is prevented.
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.
NWPU-ASLP System for the VoicePrivacy 2022 Challenge
Our system consists of four modules, including feature extractor, acoustic model, anonymization module, and neural vocoder.
