Detection of disguised speech in forensic science by humans and automatic systems
| Type of publication: | Thesis |
| Citation: | Pettinato_THESIS_2020 |
| Year: | 2020 |
| Month: | July |
| School: | Université de Lausanne Ecole des Sciences Criminelles |
| Note: | Master Thesis |
| Abstract: | The step preceding the speaker identification process consists in the determination of the authenticity of a speech sample. The focus of this thesis is on the performance of humans in detecting altered samples from replay, speech synthesis (TTS) and voice conversion (VC) systems. A listening test was constructed on the online survey platform LimeSurvey. The participants were asked to assess a series of recordings by first giving a binary evaluation (“authentic” or “altered”) and then by specifying their level of confidence on a 5-point scale. Moreover, the logic behind the aural approach was studied by inspecting the criteria used by the respondents in the assessment of the recordings. The same samples were also evaluated with an automatic LFCC-GMM-based system, trained on two different datasets, in order to make a comparison. The results show that the human’s performance (EER=0.10) surpasses the machine’s (EER=0.35 and EER=0.46) in the detection of the altered samples. However, sophisticated voice disguise systems have now reached levels of quality that can also easily fool most humans, which makes them a real threat to security and the identification process. |
| Keywords: | authentication, Biometrics, speaker recognition, Spoofing, Voice disguise |
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| Added by: | [UNK] |
| Total mark: | 0 |
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