# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 1 Introduction
The authors in [20] proposed a method of attentive statistics pooling, which computed weighted means and deviations for pooling by attention mechanism to extract speaker embeddings in a more accurate and effective way. The authors in [21] took a structured self-attention mechanism and incorporated it into the pooling layer. During the past few years, in addition to exploring different loss functions and pooling methods, network architecture design has also become an important part of improving the system. Most recent studies in this field focus on the x-vector proposed by [PERSON] et al. [19], which took the place of i-vector [22] and became the baseline for its great success. The neural network of a typical x-vector consists of three main components: (1) a front-end neural network for extracting frame-level features, (2) a pooling layer that aggregates the frame-level representations across the temporal dimension to form utterance-level statistics and (3) a speaker classifier followed by a multi-class Cross-Entropy loss. The front-end neural network could be a time-delay neural network (TDNN) [23], long short-term memory (LSTM) networks [24], convolutional neural network (CNN) [25; 26], etc. State-of-the-art SV systems architectures are ECAPA-TDNN [27] and RepVGG [28], but these structures are also based on the basic x-vector structure. Due to the simple structure of x-vector and the convenience of training, our experiments are based on the basic x-vector. In this paper, we compare the performance of various loss methods on TI-SV and propose a novel pseudo-phoneme label loss. We adopt a multi-task learning approach [29] to take both content information at the frame level and speaker information at the segment level into consideration for calculating the loss function. Unlike the c-vector [5], we just generate pseudo-phoneme labels by a simple clustering method to calculate the clustering loss. No additional annotated ASR data is required to train the ASR module. Compared with the previous methods, the main contributions of this paper are as follows:  
1. We compare the performance of existing primary loss methods on speaker verification. The loss function includes softmax loss, center loss, triplet loss, log-likelihood-ratio cost loss, additive margin softmax loss and additive angular margin loss. Based on HuBERT and DeepCluster, we propose a pseudo-phoneme label loss for TI-SV that introduces speech context information to boost speaker verification performance without an additional ASR component. We also tried different implementations of PPL loss and experimented with the VoxCeleb dataset. 3. We test the performance of PPL loss on the x-vector system. Including the combination of PPL loss with different loss functions, it is proven by experiments that our proposed PPL loss can improve the system performance. 4. Experimental results on the VoxCeleb database demonstrate the effectiveness of the proposed method. We also explored the impact of different parameter settings of PPL loss on the final performance. It was verified through experiments that when using PPL loss, different parameter settings were improved compared to the baseline system, proving the effectiveness of PPL loss. The subsequent contents of this paper are organized as follows: Section 2 depicts several common losses in the speaker recognition tasks; Section 3 presents pseudo-phoneme label loss; Section 4 illustrates our settings; Section 5 gives experimental results and analysis. The conclusions are drawn in Section 6.