# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 6 Conclusions  
In this work we have successfully analyzed the problem of short utterances as a problem of unbalanced, even missing, patterns.  
We have shown that embeddings can be partially understood as weighted sum of phoneme contributions, each of them illustrating the particularities of the speaker for the acoustic unit. When the weight distributions differ from the expected one as in short utterances, embeddings experiment shifts with respect to their original location. When these shifts are large enough they are not considered intra-speaker variability anymore and attributed to speaker mismatches. Therefore, these shifts are responsible for the loss of performance.  
Our contribution has been focused on the phonetic similarity between enrollment and test utterances. We have proposed the KL2 distance as metric for the relative phonetic distance between enrollment and test. We have also illustrated the dependencies of the score and the evaluation performance (EER, minDCF) of systems in terms of the proposed distance. Moreover, we have realized that this influence is specially noticeable in the target trials, while non-target trials are almost unaffected. Our results also indicate the existence of a range of reliable distance where degradation is bounded. Working beyond this limit makes performance degrade very fast. Furthermore, our experiments indicate that once perfect match of the distributions is achieved, further information in extra components does not provide a significant improvement in performance.  
Unfortunately, the phoneme distribution must be complemented with accurate information for all possible phonemes to be improved. Our experiments with very low relative phoneme distances, even zero, behave worse than experiments with complete information but larger enrollment-test distances. This is a consequence of the unseen phonemes, which help with the distance but not with the discrimination of speakers. Further research should be done about this missing information. Moreover, this analysis has been carried out with i-vectors. Therefore it is required experimental confirmation with other embedding representations in the state-of-the-art.  
Figure 9: Normalized distribution of scores for Target (blue) and Non-target(red) trials of scenarios Long-Short (continuous line) and Short-Short Equalized (dashed line).Experiment carried out with SRE10 “coreext-coreext det5 female”.  
**Author Contributions:** Conceptualization, I.V. and A.O.; Methodology, I.V. and A.O.; Software, I.V.; Investigation, I.V. and A.O.; Supervision A.O.; Writing-original draft preparation, I.V.; Writing-review and editing, A.O., A.M. and E.L.  
**Funding:** This work has been supported by the Spanish Ministry of Economy and Competitiveness and the European Social Fund through the project TIN2017-85854-C4-1-R, Government of Aragon (Reference Group T36_17R) and co-financed with Feder 2014-2020 \"Building Europe from Aragon\".  
**Acknowledgments:** We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU. This material is based upon work supported by Google Cloud.  
**Conflicts of Interest:** The authors declare no conflict of interest.