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Found 9 papers, 5 papers with code
Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems
Moreover, we also consider predicting the difficulty levels of algorithm problems, which can be used as useful guidance to calculate the required time to solve that problem.
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method
Inverse text normalization (ITN) is crucial for converting spoken-form into written-form, especially in the context of automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime.
Phase-Aware Spoof Speech Detection Based on Res2Net with Phase Network
The spoof speech detection (SSD) is the essential countermeasure for automatic speaker verification systems.
GRPE: Relative Positional Encoding for Graph Transformer
The former loses preciseness of relative position from linearization, while the latter loses a tight integration of node-edge and node-topology interaction.
Ranked #13 on
Graph Regression
on PCQM4Mv2-LSC
Generalizing RNN-Transducer to Out-Domain Audio via Sparse Self-Attention Layers
The domain mismatch problem for Conformer RNN-T has not been intensively investigated yet, which is an important issue for the product-level speech recognition system.
UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation
Using full-band mel-spectrograms as input, we expect to generate high-resolution signals by adding a discriminator that employs spectrograms of multiple resolutions as the input.
Accelerating RNN Transducer Inference via One-Step Constrained Beam Search
We propose a one-step constrained (OSC) beam search to accelerate recurrent neural network (RNN) transducer (RNN-T) inference.
