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Found 6 papers, 1 papers with code
Iteratively Improving Speech Recognition and Voice Conversion
Many existing works on voice conversion (VC) tasks use automatic speech recognition (ASR) models for ensuring linguistic consistency between source and converted samples.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing
Primary goal of an emotional voice conversion (EVC) system is to convert the emotion of a given speech signal from one style to another style without modifying the linguistic content of the signal.
Robust One-Shot Singing Voice Conversion
We then propose a two-stage training method called Robustify that train the one-shot SVC model in the first stage on clean data to ensure high-quality conversion, and introduces enhancement modules to the encoders of the model in the second stage to enhance the feature extraction from distorted singing voices.
Hierarchical disentangled representation learning for singing voice conversion
Conventional singing voice conversion (SVC) methods often suffer from operating in high-resolution audio owing to a high dimensionality of data.
NENET: An Edge Learnable Network for Link Prediction in Scene Text
Text detection in scenes based on deep neural networks have shown promising results.
Improving Voice Separation by Incorporating End-to-end Speech Recognition
Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
