On the limits of automatic speaker verification: Explaining degraded recognizer scores through acoustic changes resulting from voice disguise

The Journal of the Acoustical Society of America
Rosa González HautamäkiTomi Kinnunen

Abstract

In speaker verification research, objective performance benchmarking of listeners and automatic speaker verification (ASV) systems are of key importance in understanding the limits of speaker recognition. While the adoption of common data and metrics has been instrumental to progress in ASV, there are two major shortcomings. First, the utterances lack intentional voice changes imposed by the speaker. Second, the standard evaluation metrics focus on average performance across all speakers and trials. As a result, a knowledge gap remains in how the acoustic changes impact recognition performance at the level of individual speakers. This paper addresses the limits of speaker recognition in ASV systems under voice disguise using a linear mixed effects model to analyze the impact of change in long-term statistics of selected features (formants F1-F4, the bandwidths B1-B4, F0, and speaking rate) to ASV log-likelihood ratio (LLR) score. The correlations between the proposed predictive model and the LLR scores are 0.72 for females and 0.81 for male speakers. As a whole, the difference in long-term F0 between enrollment and test utterances was found to be the individually most detrimental factor, even if the ASV system uses only spectra...Continue Reading

References

Jul 25, 2007·Forensic Science International : Synergy·Cuiling Zhang, Tiejun Tan
Apr 14, 2009·Behavior Research Methods·Nivja H de Jong, Ton Wempe
Nov 1, 2007·IEEE Transactions on Audio, Speech, and Language Processing·Dagen Wang, Shrikanth S Narayanan
Mar 29, 2014·Forensic Science International : Synergy·Adrian LeemannVolker Dellwo
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Dec 6, 2016·Frontiers in Psychology·Sara Skoog Waller, Mårten Eriksson
May 4, 2017·The Journal of the Acoustical Society of America·John H L HansenNavid Shokouhi

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