# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 1 Introduction  
Speaker verification (SV), as a kind of biometric recognition technology, is used to determine an unknown speaker's identity from a segment of her/his utterance and plays an increasingly important role in the areas of access control, transaction authentication, monitoring, information retrieval, forensics, etc. Usually, it can be categorized into text-dependent speaker verification (TD-SV) and text-independent speaker verification (TI-SV) [1] to whether the spoken text is restricted or not. The TD-SV method can use the semantic information in the speech to build a more accurate model because it knows the content of the speech in advance, and this method can obtain high-performance verification results. TI-SV has high flexibility and a wide range of applications, but its performance is not as good as TD-SV under the same speech conditions. In this paper, we focus on TI-SV. Some research introduced speech information into the TD-SV system based on a multi-task learning approach. On the one hand, speaker adaptation technology is widely used in speech recognition to improve recognition accuracy, indicating that human vocal features are reflected in pronunciation information [2; 3]; on the other hand, the performance of TD-SV with fixed speech content is significantly better than that of TI-SV [4], indicating that changes in pronunciation content also have an impact on the vocal features. Therefore, consideration of both vocal and pronunciation feature in speech will help to improve the effectiveness of the speaker representation. The c-vector [5] proposes a frame-level acoustic feature extraction method based on multi-task learning of acoustic and pronunciation information. The articulation informationis fused into the frame-level vocal pattern features by partially sharing the vocal pattern feature vector extraction model with the implicit layer of the automatic speech recognition (ASR) [6; 7] model. It is necessary to train an ASR model in addition to the speaker recognition system, which not only requires a large amount of labeled data but also increases the amount of computation. At present, most of the improvements in speaker recognition system focus on improving the loss function, pooling method and network structure. In the early studies, the softmax layer and the Cross-Entropy (CE) loss function obtained good performance. The CE loss focused on measuring the difference between the real value and the predicted value. Compared with it, the center loss [8] simultaneously learns a center for each class and penalizes the distances between features and their corresponding class centers at the frame level. Refs. [9; 10; 11; 12; 13] try to enlarge the decision boundaries between classes to distinguish different speaker vectors. Most of the existing loss functions for the speaker verification task adopt a multi-class classifier at the training stage. However, most SV systems based on deep neural networks are usually trained without considering their main goal, which is making a binary decision. The triplet loss [14; 15] and the log-likelihood-ratio cost function (CLLR) [16], which can directly make the final decision without an extra back-end module, have been well experimented on several databases. The performance is improved at the cost of difficult training. In SV, pooling is often used for frame-level information aggregation in speaker recognition and verification. An averaging pooling layer is the most popular method to aggregate frame-level speaker feature vectors to obtain segment-level embedding for variable durations of speech. Subsequently, [PERSON]. [17; 18; 19] proposed a statistics pooling layer that computes not only the mean, but also the standard deviation of frame-level features. These two vectors were concatenated to form a fixed-length representation of the input utterance.