# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 1 Introduction  
Speaker recognition is the area of speech technologies that allows the automatic recognition of the speaker's identity given some portions of his/her speech. Its goal is the proper characterization of the speaker, isolating singular characteristics of his/her voice and making possible accurate comparisons among different speakers. When these comparisons involve the choice of a candidate within a closed set we talk about speaker identification. Whenever we must decide whether two speakers, enroll and test, are the same person we talk about speaker verification. In both cases we can assume the speech content to be known (e.g., a password) or not, differentiating between text-dependent and text-independent conditions respectively. The least restrictive version of the recognition problem is text-independent speaker verification, where we face no restriction about the message nor the involved speakers. The traditional strategy to tackle this challenge consists of the right characterization of the involved speakers, enrollment and test and a fair comparison of hypotheses (target and non-target) afterwards. This characterization must exploit the singularities in the voice of speakers regardless of the message content and the acoustic conditions such as noise or reverberation. Multiple alternatives of speaker characterization have been proposed, as shown in some reviews such as [1]. Some of the first proposals are based on VectorQuantization techniques [2]. Posterior contributions represent speakers according to Gaussian Mixture Models (GMMs) [3]. This idea has been evolved in Joint Factor Analysis (JFA) [4], where GMMs depend on Speaker and Channel hidden variables. Posterior evolutions merge the two hidden variables in i-vectors [5], only making use of the Total Variability subspace. Deep Neural Networks (DNNs) have also contributed, first in hybrid systems [6; 7] substituting the GMM posteriors and features by DNN-based information respectively. Finally DNNs have overcome traditional generative systems, as x-vectors [8], proposing discriminative extraction solutions for the representation of utterances. Once utterances are characterized, decisions should be made according to a score. This score is intended to determine how likely both enroll and test speakers are the same or not. Current state-of-the-art opts for extracting some embedded representations from the acoustic utterances. These embedded representations are later used for evaluation purposes making use of backend systems. A popular backend is Probabilistic Linear Discriminant Analysis (PLDA) [9], which allows the Gaussian treatment of the process. Other alternatives have also been proposed, such as the pairwise Support Vector Machines (SVM) [10]. Historically impulsed by National Institute of Standards and Technology (NIST) Speaker Recognition Evaluations (SRE) campaigns, speaker recognition was developed for a very specific condition: Long utterances with a large amount of telephone speech from a single speaker. In this scenario speaker recognition has constantly improved its capabilities and reduced the error while gaining robustness. However, when utterances get shorter the performance with these techniques is severely degraded. This issue is gaining relevance because the short utterance scenario is becoming more and more common. Conversational speech is composed of interleaved relatively short contributions (1-30 s approximately depending on the domain) from the different speakers. The identification of these short contributions, that is, the diarization task, usually works with even shorter segments (1-3 s) to accurately deal with speaker boundaries. Hence improvements in this scenario are becoming more and more needed. In this work we try to provide a better understanding about the embedding space. Our study includes the procedures to extract information from an utterance and its projection on the embedding subspace. Our work also analyzes the consequences of these extraction approaches.