Acoustic cues for the recognition of self-voice and other-voice

Abstract

Self-recognition, being indispensable for successful social communication, has become a major focus in current social neuroscience. The physical aspects of the self are most typically manifested in the face and voice. Compared with the wealth of studies on self-face recognition, self-voice recognition (SVR) has not gained much attention. Converging evidence has suggested that the fundamental frequency (F0) and formant structures serve as the key acoustic cues for other-voice recognition (OVR). However, little is known about which, and how, acoustic cues are utilized for SVR as opposed to OVR. To address this question, we independently manipulated the F0 and formant information of recorded voices and investigated their contributions to SVR and OVR. Japanese participants were presented with recorded vocal stimuli and were asked to identify the speaker—either themselves or one of their peers. Six groups of 5 peers of the same sex participated in the study. Under conditions where the formant information was fully preserved and where only the frequencies lower than the third formant (F3) were retained, accuracies of SVR deteriorated significantly with the modulation of the F0, and the results were comparable for OVR. By contrast, under a condition where only the frequencies higher than F3 were retained, the accuracy of SVR was significantly higher than that of OVR throughout the range of F0 modulations, and the F0 scarcely affected the accuracies of SVR and OVR. Our results indicate that while both F0 and formant information are involved in SVR, as well as in OVR, the advantage of SVR is manifested only when major formant information for speech intelligibility is absent. These findings imply the robustness of self-voice representation, possibly by virtue of auditory familiarity and other factors such as its association with motor/articulatory representation.

The effects of stimulus complexity on the preattentive processing of self-generated and nonself voices: An ERP study

“…We found no association between a voice's acoustic properties and the amplitudes of both the MMN and P3a to an SGV, yet earlier evidence had demonstrated that both F0 and formant frequencies are critical acoustic cues underlying successful voice identity recognition (e.g., Latinus et al, 2013;Xu et al, 2013). As expected, this lack of association was plausibly due to the fact that our ERP analysis controlled for the physical differences between the voice stimuli by using a Blike from like^subtraction approach.…”

“…Even in these challenging situations, speakers automatically and effortlessly identify the voice they are hearing as their own (Keenan, Falk, & Gallup, 2003;Xu, Homae, Hashimoto, & Hagiwara, 2013).…”

“…In order to manipulate stimulus complexity, two stimulus categories-vocalization (VCC) and word (WCC) category conditions-were used that differed in terms of their durations (i.e., 300 vs. 501 ms) and phonetic variability (i.e., vowel vs. disyllabic word). Furthermore, recent studies have shown that listeners strongly rely on both perceived pitch (fundamental frequency: F0) and formant frequencies to extract voice identity information (Baumann & Belin, 2010;Belin, Bestelmeyer, Latinus, & Watson, 2011;Belin, Fecteau, & Bédard, 2004;Latinus, McAleer, Bestelmeyer, & Belin, 2013;Schweinberger, Kawahara, Simpson, Skuk, & Zäske, 2014;Schweinberger, Walther, Zäske, & Kovács, 2011;Xu et al, 2013). The existing evidence suggests that each speaker's voice is coded within temporal voice-sensitive regions as a function of its physical acoustic deviation regarding an internal vocal prototype (Latinus & Belin, 2012;Latinus et al, 2013;Schweinberger et al, 2011): More acoustically distant voices are not only perceived as being more Bdistinctive^ (Baumann & Belin, 2010;, but they also elicit increased activation within these temporal voice-sensitive regions (Latinus et al, 2013).…”

Words Get in the Way: Linguistic Effects on Talker Discrimination

“…The ability to discriminate between individuals solely from their vocalizations is found in many species (e.g., mouse‐eared bats, bottlenose dolphins) (Janik, Sayigh, & Wells, ; Yovel, Melcon, Franz, Denzinger, & Schnitzler, ). In humans, the recognition of individuals from voice alone is determined by a multidimensional suite of acoustic characteristics unique to the talker, including spectral envelope and its change over time, fluctuation in fundamental frequency and amplitude, moments of periodicity and aperiodicity, and long‐term averaged spectrum (e.g., Bricker & Pruzansky, ; Fant, ; Hecker, ; Hollien & Klepper, ; Xu, Homae, Hashimoto, & Hagiwara, ). As it is clear that the speech signal carries both linguistic (e.g., phonological, lexical) information (Allopenna, Magnuson, & Tanenhaus, ; Gaskell & Marslen‐Wilson, ) as well as acoustic correlates to talker identity (e.g., Creel, Aslin, & Tanenhaus, ; Nygaard, Sommers, & Pisoni, ), a growing psycholinguistic literature seeks to understand the interaction between levels of abstraction of the speech signal and the cognitive processes involved in speech perception, word recognition, and word learning.…”

Expectancy changes the self-monitoring of voice identity

“…Although recordings of self-voice can produce a feeling of eeriness for listeners as compared to when spoken (Kimura et al, 2018), people nevertheless recognize recorded voice samples as their own (Nakamura et al, 2001;Kaplan et al, 2008;Rosa et al, 2008;Hughes & Nicholson, 2010;Xu et al, 2013;Candini et al, 2014;Pinheiro et al, 2016aPinheiro et al, , 2016bPinheiro et al, , 2019. However, in ambiguous conditions (i.e.…”