Additive Margin Sincnet For Speaker Recognition

Abstract

Speaker Recognition is a challenging task with essential applications such as authentication, automation, and security. The SincNet is a new deep learning based model which has produced promising results to tackle the mentioned task. To train deep learning systems, the loss function is essential to the network performance. The Softmax loss function is a widely used function in deep learning methods, but it is not the best choice for all kind of problems. For distance-based problems, one new Softmax based loss function called Additive Margin Softmax (AM-Softmax) is proving to be a better choice than the traditional Softmax. The AM-Softmax introduces a margin of separation between the classes that forces the samples from the same class to be closer to each other and also maximizes the distance between classes. In this paper, we propose a new approach for speaker recognition systems called AM-SincNet, which is based on the SincNet but uses an improved AM-Softmax layer. The proposed method

Stats

• citations8
• S2 citations—
• github stars0
• HF likes0
• heat score7.16
• arxiv keynunes2019additive

Related papers

• Curricular Sincnet: Towards Robust Deep Speaker Recognition By Emphasizing Hard Samples In Latent Space (2021)4.52
• Speaker Recognition From Raw Waveform With Sincnet (2018)20.65
• Speech And Speaker Recognition From Raw Waveform With Sincnet (2018)0.00
• Large Margin Softmax Loss For Speaker Verification (2019)14.66
• Margin Matters: Towards More Discriminative Deep Neural Network Embeddings For Speaker Recognition (2019)15.25
• Experimenting With Additive Margins For Contrastive Self-supervised Speaker Verification (2023)4.52
• Angular Softmax Loss For End-to-end Speaker Verification (2018)11.19
• Additive Margin In Contrastive Self-supervised Frameworks To Learn Discriminative Speaker Representations (2024)2.26