Age-Related Voice Disguise and its Impact on Speaker Verification Accuracy

Abstract

This study focuses in the impact of age-related intentional voice modification, or age disguise, on the performance of automatic speaker verification (ASV) systems. The data collected for this study includes 60 native Finnish speakers (29 males, 31 females) with age range between 18 and 73 years. The corpus consist of two sessions of read speech per speaker. Our experiments demonstrate vulnerability of modern ASV systems when a person attempts to conceal his or her identity, by modifying the voice to sound like an old or young person. For our i-vector PLDA system, the increase in equal error rate (EER), in the case of male speakers, was 7-fold for the attempt of old voice and 11-fold for young voice. Similar degradation in performance is observed for female speakers with a 5-fold increase in EER for old voice disguise and a 6-fold increase for young voice disguise. We further analyze the factors affecting the performance of ASV systems for the studied speech data. In our experiments, male speakers were found more successful in disguising their voices. The effect on fundamental frequency (F 0) was also studied. The mean F 0 distributions showed a shift towards higher frequencies when speakers attempted a young voice, which relates to the perception that younger speakers' F 0 values tend to be higher than for older speakers.

Acoustical and perceptual study of voice disguise by age modification in speaker verification

“…Voice disguise is a complex problem that has attracted interest from different research communities. Previous studies on the topic enable one to identify Our preliminary analyses of the effects of voice disguise on modern ASV 60 systems was reported in (González Hautamäki et al, 2016). The experiments indicated the vulnerability of our ASV systems in the presence of disguised voices when the speakers intended old and young voices.…”

“…The second element is to compare the performance of native and non-native listeners for its relevance in a forensic setting such as voice-lineups, in which the listeners may be unfamiliar with the speaker's language. Previous studies confirm that the reliability of non-native listeners decreases in speaker recognition tasks (Eriksson 100 et al, 2010;Köster et al, 1997) The dataset used for this study was collected by the authors and is the same that was used in our preliminary study (González Hautamäki et al, 2016). Our data consists of speech from 60 native Finnish speakers with 31 female and 110 29 male speakers.…”

“…In addition to the acoustic analysis, we designed a perceptual experiment to benchmark the performance of human speaker verification accuracy under 90 voice disguise. Our perceptual task includes two novel elements, first, a selection of speech sample pairs, or trials, using the results from the ASV systems implemented in our previous study (González Hautamäki et al, 2016). More specifically, we use the ASV system output to select easy, intermediate and difficult speaker pairs.…”

“…In the same context, our previous study (González Hautamäki et al, 2016) 190 evaluates the performance of six ASV systems. In terms of equal error rate (EER), the ASV systems' configuration performance was degraded with disguised voices.…”

“…It therefore only considers the close-talking microphone speech, which has the highest recording quality. Interested readers are pointed to our earlier study (González Hautamäki et al, 2016) in which we analyzed the effect of smart-phone recordings on the accuracy of automatic speaker recogni-400 tion. The recording set-up is illustrated in Figure 3.…”