Search Results for author:
Found 16 papers, 3 papers with code
Learning Speech Representation From Contrastive Token-Acoustic Pretraining
However, existing contrastive learning methods in the audio field focus on extracting global descriptive information for downstream audio classification tasks, making them unsuitable for TTS, VC, and ASR tasks.
Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding
However, existing methods suffer from three problems: the high dimensionality and waveform distortion of discrete speech representations, the prosodic averaging problem caused by the duration prediction model in non-autoregressive frameworks, and the information redundancy and dimension explosion problems of existing semantic encoding methods.
Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection
Self-supervised speech models are a rapidly developing research topic in fake audio detection.
Adaptive Fake Audio Detection with Low-Rank Model Squeezing
During the inference stage, these adaptation matrices are combined with the existing model to generate the final prediction output.
UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion
Text-to-speech (TTS) and voice conversion (VC) are two different tasks both aiming at generating high quality speaking voice according to different input modality.
Emotion Selectable End-to-End Text-based Speech Editing
To achieve this task, we propose Emo-CampNet (emotion CampNet), which can provide the option of emotional attributes for the generated speech in text-based speech editing and has the one-shot ability to edit unseen speakers' speech.
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection
Some scene fake audio detection benchmark results on the SceneFake dataset are reported in this paper.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research.
System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation
The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious content manipulation.
An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio
Many effective attempts have been made for fake audio detection.
Fully Automated End-to-End Fake Audio Detection
The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure.
NeuralDPS: Neural Deterministic Plus Stochastic Model with Multiband Excitation for Noise-Controllable Waveform Generation
We have also verified through experiments that this method can effectively control the noise components in the predicted speech and adjust the SNR of speech.
CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing
It can solve unnatural prosody in the edited region and synthesize the speech corresponding to the unseen words in the transcript.
ADD 2022: the First Audio Deep Synthesis Detection Challenge
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021.
Singing-Tacotron: Global duration control attention and dynamic filter for End-to-end singing voice synthesis
Firstly, we propose a global duration control attention mechanism for the SVS model.
Half-Truth: A Partially Fake Audio Detection Dataset
Therefore, this paper develops such a dataset for half-truth audio detection (HAD).
