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DrivingScene reconstructs dynamic 4D driving scenes online from two surround-view frames with strong quality and efficiency.

PAGS uses semantic priorities in Gaussian Splatting to improve dynamic driving-scene quality and efficiency, achieving high fidelity with very fast rendering.

SonicSim is a customizable simulation toolkit and benchmark that improves real-world generalization for moving-source speech processing.

This paper presents a novel neural vocoder called InstructSing, which can converge much faster compared with other neural vocoders while maintaining good performance by integrating differentiable digital signal processing and adversarial training.

We propose an integrated spoofing-aware ASV framework robust to domain and channel mismatch, with strong gains on the CNComplex benchmark.

This paper presents CrossSinger, a cross-lingual singing voice synthesizer based on Xiaoicesing2. It tackles the challenge of creating a multi-singer high-fidelity singing voice synthesis system with cross-lingual capabilities using only monolingual singers during training. The system unifies language representation, incorporates language information, and removes singer biases. Experimental results show that CrossSinger can synthesize high-quality songs for different singers in various languages, including code-switching cases.

This paper introduces HiFi-WaveGAN, a system designed for real-time synthesis of high-quality 48kHz singing voices from full-band mel-spectrograms. It improves upon WaveNet with a generator, incorporates elements from HiFiGAN and UnivNet, and introduces an auxiliary spectrogram-phase loss to enhance high-frequency reconstruction and accelerate training. HiFi-WaveGAN outperforms other neural vocoders like Parallel WaveGAN and HiFiGAN in quality metrics, with faster training and better high-frequency modeling.

The ability of countermeasure models to generalize from seen speech synthesis methods to unseen ones has been investigated in the ASVspoof challenge. However, a new mismatch scenario in which fake audio may be generated from real audio with unseen genres has not been studied thoroughly. To this end, we first use five different vocoders to create a new dataset called CN-Spoof based on the CN-Celeb1&2 datasets. Then, we design two auxiliary objectives for regularization via meta-optimization and a genre alignment module, respectively, and combine them with the main anti-spoofing objective using learnable weights for multiple loss terms.

This paper presents XiaoiceSing2, an enhanced singing voice synthesis system that addresses over-smoothing issues in middle- and high-frequency areas of mel-spectrograms. It employs a generative adversarial network (GAN) with improved model architecture to capture finer details.

Recent advances in Transformer-based models have enhanced end-to-end automatic speech recognition (ASR), enabling deployment on smart devices. However, these models often require large numbers of parameters. To address this, we introduce a block-reusing strategy for speech Transformers (BRST) and an adapter module (ADM) for efficient parameter use in small footprint ASR systems, without sacrificing accuracy. Tested on the AISHELL-1 corpus, our method achieved low character error rates with significantly fewer parameters, demonstrating both efficiency and effectiveness, particularly with the inclusion of ADM.