A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement

It is promising to design a single model that can suppress various distortions and improve speech quality, i.e., universal speech enhancement (USE). Compared to supervised learning-based predictive methods, diffusion-based generative models have shown greater potential due to the generative capacities from degraded speech with severely damaged information. However, artifacts may be introduced in highly adverse conditions, and diffusion models often suffer from a heavy computational burden due to many steps for inference. In order to jointly leverage the superiority of prediction and generation and overcome the respective defects, in this work we propose a universal speech enhancement model called PGUSE by combining predictive and generative modeling. Our model consists of two branches: the predictive branch directly predicts clean samples from degraded signals, while the generative branch optimizes the denoising objective of diffusion models. We utilize the output fusion and truncated diffusion scheme to effectively integrate predictive and generative modeling, where the former directly combines results from both branches and the latter modifies the reverse diffusion process with initial estimates from the predictive branch. Extensive experiments on several datasets verify the superiority of the proposed model over state-of-the-art baselines, demonstrating the complementarity and benefits of combining predictive and generative modeling.

The USTC-NERCSLIP Systems for The ICMC-ASR Challenge

This report describes the submitted system to the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) challenge, which considers the ASR task with multi-speaker overlapping and Mandarin accent dynamics in the ICMC case. We implement the front-end speaker diarization using the self-supervised learning representation based multi-speaker embedding and beamforming using the speaker position, respectively. For ASR, we employ an iterative pseudo-label generation method based on fusion model to obtain text labels of unsupervised data. To mitigate the impact of accent, an Accent-ASR framework is proposed, which captures pronunciation-related accent features at a fine-grained level and linguistic information at a coarse-grained level. On the ICMC-ASR eval set, the proposed system achieves a CER of 13.16% on track 1 and a cpCER of 21.48% on track 2, which significantly outperforms the official baseline system and obtains the first rank on both tracks.