## Contributions
### Speaker Verification and Identification  
Speech classification and identification is a research area that has consistently been highly represented in IberSPEECH conferences, including the 2018 edition. This special issue includes two papers from ViVoLab [4; 5]. In these works, the authors investigate how to use phonetic information for the tasks of short sentence verification and text-independent speaker verification using Neural Networks. Moreover, the paper from [PERSON] et al. [6] explores the use of Boltzmann Machines to speaker clustering and tracking. Furthermore, the research conducted by [PERSON] et al. [7] apply speaker identification techniques in a real application under adverse conditions (see Section 2.6).  
[PERSON] et al. [4] analyze the problem of short sentences in Speaker Characterization and, in particular, in Speaker Identification. It is well known that the performance of these systems degrades with short sentences. Most of state-of-the-art systems, including i-vectors and Deep Neural Networks (DNN) vectors, apply a scoring function based on test and reference vectors. The authors observe that these vectors can be expressed as the weighted average of the vectors describing each phoneme. A meaningful comparison between test and reference vectors requires a good estimation not only of the phoneme characterization, but also of the relative frequency of each phoneme. In fact, they show that in short sentences the mismatch in phoneme frequencies between test and reference is the main cause of error. They show that short utterances (3-60 s) can perform almost with the same accuracy than long sentences if there is a match between phonetic distribution.  
[PERSON] et al. [5] propose an architecture to include phonetic information in text-dependent verification systems using Neural Networks. DNN has improved significantly many speaker verification tasks. However, these models do not offer the same boost in performance in the case of text-dependent verification systems, when the text is part of the identity information. The problem with traditional DNN is that the pooling mechanism used to transform the acoustic sequence in a fixed-dimension vector does not preserve the phonetic order, therefore disregarding this important source of information. This paper combines DNN with an external alignment which can be computed using HMM-based forced alignment. The model first applies 1D convolutional networks to get a more powerful representation of the frames. Then, the frames associated with the same HMM-state are averaged to produce a fixed-length vector. Finally, a projection layer reduces the vector dimension and the cosine metric is applied. This architecture achieves much better results than the traditional average, resulting in competitive results.  
[PERSON] et al. [6] apply features derived using the Restricted Bolzmann machine (RBM) to the task of speaker clustering and tracking. Previously, the authors had successfully shown that RBM can offer competitive results in speaker recognition. Similarly, in this paper, the speaker is represented using a RBM vector, which is derived applying PCA whitening to the visible to hidden weight matrices of the RBM. For speaker clustering, an Agglomerative Hierarchical Clustering algorithm is applied and the results in terms of Equal Impurity show a relative 11% improvement with respect to i-vectors. Similarly, using RBM vectors in the identification step of a speaker tracking algorithm, the results are also better than i-vectors both using cosine distance or PLDA as scoring function.