## 1 Introduction  
Recently, techniques based on discriminative deep neural networks (DNN) have achieved substantial success in many speaker verification tasks. These techniques follow the philosophy of the state-of-the-art face verification systems [1; 2] where embeddings are usually extracted by reduction mechanisms and the decision process is based on a similarity metric [3]. Unfortunately, in text-dependent tasks, this approach does not work efficiently since the pronounced phrase is part of the identity information [4; 5]. A possible cause of the inaccuracy in text-dependent tasks could be derived from using the temporal average as a representation of the whole utterance as we show in the experimental section. To solve this problem, this paper presents a new architecture which combines a deep neural network with a phonetic phrase alignment method used as a new internal layer to maintain the temporal structure of the utterance. As we will show, it is a more natural solution for the text-dependent speaker verification since the speaker and phrase information can be encoded in the supervector thanks to the neural network and the specific states of the supervector. A supervector is a concatenation of smaller-dimensional vectors from each specific state into a higher-dimensional vector. In the context of text-independent speaker verification tasks, the baseline system based on i-vector extraction and Probabilistic Linear Discriminant Analysis (PLDA) [6; 7] is still among the best results of the state-of-the-art. An i-vector is a representation of an utterance in a low-dimensional subspace called the total variability subspace, and the PLDA model produces the verification scores. However, as we previously mentioned, many improvements on this baseline system have been achieved in recent years by progressively substituting components of the systems by DNNs, thanks to their greater expressiveness and the availability of large databases. Examples of this are the use of DNN bottleneck representations as features replacing or combined with spectral parametrization [8], training DNN acoustic models to use their outputs as posteriors for alignment instead of Gaussian Mixture Model (GMM) in i-vector extractors [9], or replacing PLDA by a DNN [10]. Other proposals similar to face verification architectures have been more ambitious and have trained a discriminative DNN for multiclass classification. Once these systems are trained, the embeddings, which are fixed-length utterance level representations, are extracted by reduction mechanisms from the DNN [11; 12; 13]. In this context, they are called x-vectors--for example, taking the average of an intermediate layer usually named the bottleneck layer. After that embedding extraction, the verification score is obtained by a similarity metric such as cosine similarity [11] or PLDA. The application of DNNs and the same techniques as in text-independent models for text-dependent speaker verification tasks has produced mixed results. On the one hand, specific modifications of the traditional techniques have been shown to be successful for text-dependent tasks such as i-vector+PLDA/Support Vector Machines (SVM) [14; 15; 16], DNNs bottleneck as features for i-vector extractors [17] or posterior probabilities for i-vector extractors [17; 18]. On the other hand, speaker embeddings obtained directly from a DNN have provided good results in tasks with large amounts of data and a single phrase [19], but they have not been as effective in tasks with more than one pass phrase and smaller database sizes [4; 5]. The lack of data in this last scenario may lead to problems with deep architectures due to overfitting of models. Another reason that we explore in the paper for the lack of effectiveness of these techniques in general text-dependent tasks is that the phonetic content of the uttered phrase is relevant for the identification.