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Lingling Dai

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3 papers

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AAAI Conference 2026 Conference Paper

DegVoC: Revisiting Neural Vocoder from a Degradation Perspective

Andong Li
Tong Lei
Lingling Dai
Kai Li
Rilin Chen
Meng Yu
Xiaodong Li
Dong Yu

Existing neural vocoders have demonstrated promising performance by leveraging Mel-spectrum as an acoustic feature for conditional audio generation. Nonetheless, they remain constrained by an inherent ``performance-cost'' dilemma that significantly hinders the development of this field. This paper revisits this foundational task from a novel degradation perspective, where Mel-spectrum is regarded as a special signal degradation process from the target spectrum. Drawing inspiration from traditional sparse signal recovery problems, we propose DegVoC, a GAN-based neural vocoder with a two-step solution procedure. First, by exploiting degradation priors, we attempt to retrieve the initial spectral structure from Mel-domain representations as an initial solution via a simple linear transformation. Based on that, we introduce a deep prior solver that accounts for the heterogeneous distribution of sub-bands in the time-frequency domain. A convolution-style attention module with a large kernel size is specially devised for efficient inter-frame and inter-band contextual modeling. With 3.89 M parameters and substantially reduced inference complexity, DegVoC achieves state-of-the-art performance across objective and subjective evaluations, outperforming existing GAN-, DDPM- and flow-matching-based baselines.

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AAAI Conference 2026 Conference Paper

GOMPSNR: Reflourish the Signal-to-Noise Ratio Metric for Audio Generation Tasks

Lingling Dai
Andong Li
Cheng Chi
Yifan Liang
Xiaodong Li
Chengshi Zheng

In the field of audio generation, signal-to-noise ratio (SNR) has long served as an objective metric for evaluating audio quality. Nevertheless, recent studies have shown that SNR and its variants are not always highly correlated with human perception, prompting us to raise the questions: Why does SNR fail in measuring audio quality? And how to improve its reliability as an objective metric? In this paper, we identify the inadequate measurement of phase distance as a pivotal factor and propose to reformulate SNR with specially designed phase-distance terms, yielding an improved metric named GOMPSNR. We further extend the newly proposed formulation to derive two novel categories of loss function, corresponding to magnitude-guided phase refinement and joint magnitude-phase optimization, respectively. Besides, extensive experiments are conducted for an optimal combination of different loss functions. Experimental results on advanced neural vocoders demonstrate that our proposed GOMPSNR exhibits more reliable error measurement than SNR. Meanwhile, our proposed loss functions yield substantial improvements in model performance, and our well-chosen combination of different loss functions further optimizes the overall model capability.

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AAAI Conference 2026 Conference Paper

SLD-L2S: Hierarchical Subspace Latent Diffusion for High-Fidelity Lip to Speech Synthesis

Yifan Liang
Andong Li
Kang Yang
Guochen Yu
Fangkun Liu
Lingling Dai
Xiaodong Li
Chengshi Zheng

Although lip-to-speech synthesis (L2S) has achieved significant progress in recent years, current state-of-the-art methods typically rely on intermediate representations such as mel-spectrograms or discrete self-supervised learning (SSL) tokens. The potential of latent diffusion models (LDMs) in this task remains largely unexplored. In this paper, we introduce SLD-L2S, a novel L2S framework built upon a hierarchical subspace latent diffusion model. Our method aims to directly map visual lip movements to the continuous latent space of a pre-trained neural audio codec, thereby avoiding the information loss inherent in traditional intermediate representations. The core of our method is a hierarchical architecture that processes visual representations through multiple parallel subspaces, initiated by a subspace decomposition module. To efficiently enhance interactions within and between these subspaces, we design the diffusion convolution block (DiCB) as our network backbone. Furthermore, we employ a reparameterized flow matching technique to directly generate the target latent vectors. This enables a principled inclusion of speech language model (SLM) and semantic losses during training, moving beyond conventional flow matching objectives and improving synthesized speech quality. Our experiments show that SLD-L2S achieves state-of-the-art generation quality on multiple benchmark datasets, surpassing existing methods in both objective and subjective evaluations.

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