# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 2 Short Utterances as Occluded Utterances  
The short utterance problem is widely known within the speaker recognition community [11]. The evaluation of trials by means of short utterances involves a severe degradation of performance. However, there is no standard definition of short utterance in the literature. While some works have reported losses of performance with audios containing less than 30 s of speech, a more severe degradation is obtained considering shorter utterances (less than 10 s) [12; 13]. This short utterance problem has also been analyzed in the the Speaker Recognition Evaluations (SRE), proposed by NIST. Despite traditionally considering utterances with more than 2 min of audio, some of the evaluations [14; 15] also include a condition in which utterances contain less than 10 s.  
This loss of performance is a consequence of a higher intra-speaker variability in the estimations with short utterances. In the literature multiple contributions have been proposed to the different steps of the speaker verification pipeline, aiming to reduce the undesired variability. The feature extraction step has been studied in different ways, attempting to provide an alternative to traditional Mel Frequency Cepstral Coefficients (MFCCs). In Reference [16] a multi resolution time-frequency feature extraction was proposed, carrying out a multi-scaled Discrete Cosine Transform (DCT) on the spectrogram, combining the information afterwards. Alternative works like that in Reference [17] fuse different features based on the amplitude and phase of the spectrum. Other contributions are focused on the modelling stage. Factor Analysis approaches were considered in Reference [18] to develop subspace models to better work with the short utterances. When considering i-vector representations, compensation techniques such as those in References [19; 20] project the obtained representations into subspaces with low variability due to short utterances. In Reference [21], it is shown that systems trained on short utterances should compensate better the uncertainty due to limited audio, evaluating better short audios. However, when systems must deal with audios with unrestricted length, systems should be trained on long utterances for a better performance. The balance of the Baum Welch statistics, required for the extraction of i-vectors, is also proposed in Reference [22]. Besides, DNNs have also mapped short-utterance i-vectors with respect to their long-utterance counterparts [23]. Other contributions have also worked on the backend, specially PLDA. In Reference [24; 25] the PLDA model includes an extra term to compensate the uncertainty of the i-vector, which depends on the utterance length. Finally, other strategies compensate the obtained score according to reliability metrics of the involved utterances [26; 27], specially its duration. This idea is extended in Reference [28], where the Quality Measure Function (QMF) term studies the interaction between enrollment and test utterances. In Reference [29], intervals of confidence are estimated, leading towards considerable accuracy. Some works, such as that in Reference [30], have studied the impact of the different phonetic content in the embedding representations. According to their results, vowels and nasal phonemes are helpful for discrimination matters. By contrast, other types of phonemes, such as fricatives or plosives, can be misleading during evaluation. Our hypothesis of work applies this idea of phonemes to short utterances. The presence of certain acoustic units boosts the performance of speaker recognition systems. However, these boosting phonemes must be in both enroll and test utterances to be effective. This match in the phonetic information goes beyond the presence of certain phonemes, also requiring a match in the phonetic proportion along the utterance. In order to explain our perspective let's make an analogy of the short utterance problem with a similar problem, face recognition with occlusions.