3 accepted papers
Noisy labels are inevitable in real-world datasets. These noisy labels cause deep neural networks to gradient descent towards the wrong direction, leading to performance degradation. In this paper, We propose an efficient method for filtering out noisy labels during training. We calculate an embeddi…
The Mixture of Experts (MoE) model is a promising approach for handling code-switching speech recognition (CS-ASR) tasks. However, the existing CS-ASR work on MoE has yet to leverage the advantages of MoE’s parameter scaling ability fully. This work proposes DLG-MoE, a Dynamic Language Group-based M…
Recently, flow matching based speech synthesis has significantly enhanced the quality of synthesized speech while reducing the number of inference steps. In this paper, we introduce SlimSpeech, a lightweight and efficient speech synthesis system based on rectified flow. We have built upon the existi…