Concluding remarks In this chapter, we firstly reminded readers of the controversial aspects of forensic speaker comparison, mainly because of the intrinsic nature of the voice, which is very different from physical biometrics like DNA or finger-prints� We highlighted the importance of the Bayesian decision framework, which has become the standard paradigm for forensics in general and for forensic speaker comparison specifically� We went deeper into the question of the use of automatic systems in forensic applications� We acknowledged the impressive progresses achieved in the field of automatic speaker recognition during the last decade, but we also pointed out several important weaknesses in the evaluation protocols� We then went back to the speaker-specific nature of the information used by automatic systems� Clearly, some doubts about automatic systems remain as demonstrated for instance by  Kahn et al� (2010)  and  Bonastre et al� (2007) � It is particularly true if we use the broader perspective of "dependability" (Avizienis et al�, 2004), which takes the whole process into account� It is important to have a comprehensive picture of forensic speaker recognition processes�  Campbell et al� (2009)  reported the importance of calibration and  Bousquet et al� (2014)  showed that normalization in the iVector domain also plays a major role for the performance of a system, although there is still no theoretical explanation for this� Previous findings on automatic speaker recognition should not give the reader the wrong impression about the use of automatic approaches in forensic speaker recognition versus human-based approaches� If automatic approaches present some weaknesses, they are unavoidable in order to assert the scientific nature of forensic speaker comparison� We do not know whether it will be possible in the future to propose a fully automatic system for forensic speaker recognition, which would follow strong scientific guidelines like Daubert's rules 15 � But we think it is quite impossible to meet such scientific rules without automatic processes as the typicality of each speaker-specific criterion 16 has to be assessed on very large databases� Emerging studies on tools and methods for computer-assisted approaches, like the SPAAT tool 17 used by USSS-MITLL during their HASR 2010 participation  (Schwartz, 2010) , demonstrate the interest of such an approach� However, our intention is not to dismiss human expert knowledge and manual approaches� Once again, Daubert's case offers a nice proposal: "If scientific rules are not fulfilled, a witness qualified as an expert by knowledge, skill, experience, training, or education, may testify in the form of an opinion"� We fully support this statement and we wish to emphasize this distinction between an expert's opinion and a scientifically-assessed method� Finally, it is interesting to see that  Campbell et al�'s (2009)  conclusions are quite close to the 2003 conclusions� The main one concerns the "caution" message: "Looking at the different points highlighted in this article, we affirm that forensic applications of speaker recognition should still be taken under a necessary need for caution.