Generative AI

We introduce a novel token-based text-to-speech (TTS) model with 0.8B parameters, trained on a mix of real and synthetic data totaling 650k hours, to address issues like pronunciation accuracy and style consistency. This model integrates a latent variable sequence with enhanced acoustic information into the TTS system, reducing errors and style changes. Our training includes data augmentation for improved timbre consistency, and we use a few-shot voice conversion model to generate diverse voices. This approach enables learning of one-to-many mappings in speech, ensuring both diversity and timbre consistency. Our model outperforms VALL-E in pronunciation, style maintenance, and timbre continuity.

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.

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.

We introduce SSI-Net, our submission to the ICASSP 2023 Speech Signal Improvement (SSI) Challenge, designed for real-time communication systems. SSI-Net features a multi-stage architecture, beginning with a time-domain restoration generative adversarial network (TRGAN) for initial speech restoration. In the second stage, we use a lightweight multi-scale temporal frequency convolutional network with axial self-attention (MTFAA-Lite) for fullband speech enhancement. In subjective tests on the SSI Challenge blind test set, SSI-Net achieved a P.835 mean opinion score (MOS) of 3.190 and a P.804 MOS of 3.178, ranking 3rd in tracks 1&2.