The impact of mismatched recordings on an automatic-speaker-recognition system and human listeners

Datos Bibliográficos

ID21550641
AutoresTomáš Nechanský (Charles University), Tomáš Bořil (0000-0002-5839-313X, Charles University), Alžběta Houzar (Charles University), Radek Skarnitzl (0000-0002-5682-1962, Charles University)
Año2023
Volumen2022
Número1
Páginas11-22
Fecha de publicación2023-01-17
Open AccessSí
TipoARTICLE
RevistaAUC PHILOLOGICA (JOURNAL)
Identificadores de la revistaISSN: 0567-8269 • E-ISSN: 2464-6830
EditorialCharles University in Prague, Karolinum Press (PUBLISHER)
DOI10.14712/24646830.2022.25
OpenAlexW4316690048
IdiomaEN
Referencias citadas22

The so-called ‘mismatch’ is a factor which experts in the forensic voice comparison field encounter regularly. Therefore, we decided to explore to what extent the automatic-speaker-recognition system’s and the earwitness’ ability to identify speakers is influenced when recordings are acquired in different languages and at different times. 100 voices in a database of 300 recordings (100 speakers recorded in three mutually mismatched sessions) were compared with an automatic-speaker-recognition software VOCALISE based on i-vectors and x-vectors, and by 39 respondents in simulated voice parades. Both the automatic and perceptual approach seem to have yielded similar results in that the less complex the mismatch type, the more successful the identification. The results point to the superiority of the x-vector approach, and also to varying identification abilities of listeners

Perception · Speaker identification · Speaker recognition · Speech recognition · Computer Science · Mathematics · Music and Audio Processing · Psychology · Speech and Audio Processing · Speech Recognition and Synthesis · Artificial Intelligence

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