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Found 11 papers, 4 papers with code
Comprehensive Survey of Model Compression and Speed up for Vision Transformers
Vision Transformers (ViT) have marked a paradigm shift in computer vision, outperforming state-of-the-art models across diverse tasks.
StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis
Moreover, existing SVS methods encounter a decline in the quality of synthesized singing voices in OOD scenarios, as they rest upon the assumption that the target vocal attributes are discernible during the training phase.
TextrolSpeech: A Text Style Control Speech Corpus With Codec Language Text-to-Speech Models
The dataset comprises 236, 220 pairs of style prompt in natural text descriptions with five style factors and corresponding speech samples.
Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus
High-fidelity multi-singer singing voice synthesis is challenging for neural vocoder due to the singing voice data shortage, limited singer generalization, and large computational cost.
SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation
In this work, we propose SingGAN, a generative adversarial network designed for high-fidelity singing voice synthesis.
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model
Finally, by showing a comparable performance in the emotional speech synthesis task, we successfully demonstrate the ability of the proposed model.
DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism
Singing voice synthesis (SVS) systems are built to synthesize high-quality and expressive singing voice, in which the acoustic model generates the acoustic features (e. g., mel-spectrogram) given a music score.
BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions
Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis.
Multimodal Emotion Recognition
Multimodal Sentiment Analysis
Learning Robust Heterogeneous Signal Features from Parallel Neural Network for Audio Sentiment Analysis
Audio Sentiment Analysis is a popular research area which extends the conventional text-based sentiment analysis to depend on the effectiveness of acoustic features extracted from speech.
Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST)
Another inspiring result is that CapsNet only needs a small amount of data to get excellent performance.
