A language-familiarity effect for speakerdiscrimination without comprehension
David Fleminga,1, Bruno L. Giordanoa, Roberto Caldarab, and Pascal Belina,c,daInstitute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom; bDepartment of Psychology, University of Fribourg,
1700 Fribourg, Switzerland; cDépartement de Psychologie, Université de Montréal, Montréal, QC, Canada H2V 2S9; and dInstitut des Neurosciences de La
Timone, Unite Mixte de Recherche 7289, Centre National de la Recherche Scientifique, Université Aix-Marseille, 13005 Marseille, France

The influence of language familiarity upon speaker identification iswell established, to such an extent that it has been argued that
“Human voice recognition depends on language ability” [Perrachione
TK, Del Tufo SN, Gabrieli JDE (2011) Science 333(6042):595]. However,
7-mo-old infants discriminate speakers of their mother tongue better
than they do foreign speakers [Johnson EK, Westrek E, Nazzi T,
Cutler A (2011) Dev Sci 14(5):1002–1011] despite their limited
speech comprehension abilities, suggesting that speaker discrimination may rely on familiarity with the sound structure of one’s
native language rather than the ability to comprehend speech. To
test this hypothesis, we asked Chinese and English adult participants to rate speaker dissimilarity in pairs of sentences in English
or Mandarin that were first time-reversed to render them unintelligible. Even in these conditions a language-familiarity effect wasobserved: Both Chinese and English listeners rated pairs of nativelanguage speakers as more dissimilar than foreign-language
speakers, despite their inability to understand the material. Ourdata indicate that the language familiarity effect is not based oncomprehension but rather on familiarity with the phonology of
one’s native language. This effect may stem from a mechanism
analogous to the “other-race” effect in face recognition.
voice perception| unintelligible speech | dissimilarity ratingsThe human voice carries linguistic information as well as
paralinguistic information about a speaker’s identity, andnormal listeners possess abilities to extract both types of information. The neuro-cognitive mechanisms underlying speechcomprehension and speaker recognition are dissociable, as
evidenced by cases of both patients with receptive aphasia (impaired speech comprehension but preserved speaker recognition)and patients with phonagnosia (impaired speaker recognition but
preserved speech comprehension) (1–5), as well as by differencesin the cortical networks engaged by the two abilities (6–13).
However, speech and voice identity processing also interact toa considerable degree. Speech recognition is influenced by speakervariability and familiarity: listeners better understand and remember speech spoken by familiar speakers (14–17). Conversely,
speaker identification is influenced by language familiarity: listeners are typically poorer at identifying speakers of a foreign
language. This so-called “Language-Familiarity Effect” (LFE) has
been demonstrated across a diverse range of languages (18–22)
and is behaviorally robust, persisting even after several days of
training (23).
A crucial, unresolved point of debate is whether the LFE
depends upon linguistic mechanisms involved in speech comprehension, or rather reflects the greater familiarity with the phonological structure of one’s own language without necessarily requiring an understanding of the linguistic message. On the one
hand, evidence from dyslexic participants, whose phonologicalprocessing abilities are impaired (24), supports the importance of
linguistic processing for general speaker identification abilities:
English-speaking dyslexic participants do not show the LFE, (i.e.,
better memory for English-speaking than Chinese-speaking voices)www.pnas.org/cgi/doi/10.1073/pnas.1401383111

shown by normal participants (25). On the other hand, a LFE isalready apparent in infants before they can fully comprehend
speech: 7-mo-olds notice a speaker change in their native language
but not in an unfamiliar language (26). Although results from dyslexic participants suggest a specific link between the LFE and“language ability” (25), results from infants (26) suggest that experience with the phonology of the maternal language, rather thancomprehension, may underpin the LFE. If this is the case, then theenhanced individuation of own-language speakers observed in
7-mo-olds should be observed in normal adult participants, evenfor unintelligible speech.
Here we tested this hypothesis by comparing dissimilarity ratings of own- and different-language speakers with time-reversed
speech stimuli. Note that reversing speech disrupts intelligibility,
but preserves “considerable phonetic information” (27) as well as
sufficient indexical information to enable listeners to recognize
voices (28–30). We collected speaker dissimilarity ratings from
Chinese and English listeners for all pairwise combinations of a setof Mandarin (n = 20) and English (n = 20) time-reversed speech
clips, and compared these dissimilarity ratings between groups of
speakers and listeners. If the LFE is based primarily on language
comprehension, then we should observe no interlanguage difference in discrimination performance, as time-reversal rendered all
stimuli unintelligible. Conversely, familiarity with a language’s
characteristic phonological structure may suffice to engender a
LFE for speaker discrimination, even without comprehension.
Mandarin Chinese is a tonal language, whereas English is stressbased; as such, a Mandarin speaker and an English speaker may
differ in terms of the language structure elements that they use todifferentiate speaking voices. For example, Mandarin and English
SignificanceA recent report [Perrachione TK, Del Tufo SN, Gabrieli JDE (2011)
Science 333(6042):595] shows that dyslexic individuals do notshow an advantage in the recognition of speakers of their own
language compared with speakers of a foreign language (the wellestablished “language-familiarity” effect) as typical subjects do,a finding that Perrachione et al. interpreted as evidence that “Human voice recognition depends on language ability.” Here, we refine this notion by providing, to our knowledge, the first evidenceof a language-familiarity effect in adult cross-cultural listeners withspeech rendered unintelligible by time reversal. This result showsthat the phonological aspects of language ability only, even without comprehension, can influence speaker discrimination.
Author contributions: D.F., B.L.G., R.C., and P.B. designed research; D.F. performed research; D.F. and B.L.G. analyzed data; and D.F., B.L.G., R.C., and P.B. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
1

To whom correspondence should be addressed. Email: d.fleming.1@research.gla.ac.uk.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1401383111/-/DCSupplemental.

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Edited by E. Anne Cutler, University of Western Sydney, Penrith South, New South Wales, Australia, and approved August 8, 2014 (received for review January
23, 2014)

differ in speaking fundamental frequency (31–33) and phonemic
inventories: Mandarin features around 1,300 syllables, whereas
English uses around 15,000 (34); the languages have very little
consonant overlap; and English features a high frequency andvariety of consonant clusters, whereas Mandarin has no consonant
clusters (35, 36). Time-reversal preserves the formant structure (ina “mirrored” form; ref. 27) of many phonemes and their mean
fundamental frequency, and, given that these features may differ
across the two languages in natural speech, and that they are
relatively well-preserved upon reversal, then native speakers ofboth languages may still be sensitive to these differences even
where intelligibility is disrupted. If sensitivity to such differences
drives a LFE then each group should show higher dissimilarity
ratings for pairs of voices speaking their native language than forpairs speaking the other language
Results
All possible paired combinations of voices were presented tolisteners who recorded their dissimilarity ratings via a computerized visual analog scale, ranging from 0 to 1 (where a rating of
0 corresponded to maximum perceived similarity and 1 to maximum perceived dissimilarity). Fig. 1A shows the dissimilarity
matrix averaged across English and Chinese participants, whererows/columns 1–20 correspond to native voices and rows/columns 21–40 to foreign voices. Participants rated four types ofpair: same-identity trials (where the same speaker was heard
twice within a pairing), foreign–foreign trials, native–native trials, and native–foreign trials. No sentence clip was uttered twice
within a pair. As shown in Fig. 1A, interlanguage pairs (where
presentations consisted of one native and one foreign voice)were rated as more dissimilar than all other pairs, as reflected by
the overall red color (high dissimilarity) of the upper right andlower left submatrices. Fig. 1B illustrates the differences between
each rating condition (same-identity mean = 0.16 ± 0.02 SE;
foreign–foreign mean = 0.59 ± 0.02 SE; native–native mean =0.62 ± 0.02 SE; native–foreign mean = 0.71 ± 0.02 SE). Eachparticipant’s mean ratings for each trial type were submitted toa repeated-measures ANOVA, which revealed a significant effect of pair type [F(3, 39) = 314.2, P < 0.001, η2partial = 0.89]. Post
hoc tests also revealed significant differences for all pairwise
comparisons of trial type (all P values < 0.02).
Crucially, when taking participant groupings into account, both
Chinese- and English-speaking listeners produced higher average
dissimilarity ratings for native-language voice pairs than for nonnative-language pairs (Chinese: native mean = 0.62 ± 0.03 SE,
nonnative mean = 0.60 ± 0.03 SE; English: native mean = 0.61 ±0.02 SE, nonnative mean = 0.57 ± 0.02 SE) (Fig. 1 B and C). We
submitted these ratings to a 2 × 2 mixed-measures ANOVA, withlistener language and speaker language as the between- andwithin-group (repeated) measures, respectively. A significant interaction between speaker and listener’s language was observed,
indicating that native-language dissimilarity ratings were higher,
regardless of the language group of the listener [F(1, 38) = 11.13,
P = 0.002, η2partial = 0.23]. The main effects of both listener andspeaker language were not significant (P values > 0.2), suggestingthat there were no statistical differences in rating behavior between groups and that both sets of voices elicited similar rating
behavior. Paired t tests confirmed our prediction that both listenergroups rated own-language pairs as more dissimilar than differentlanguage pairs (Chinese-speaking participants: Chinese > English[t(19) = 2.57, P = 0.02, Cohen’s d = 0.17]; English-speaking participants: English > Chinese [t(19) = 2.36, P = 0.03, Cohen’s d =0.41]). To investigate the robustness of these results, we computed
bootstrapped 95% confidence intervals of the native > foreign

Fig. 1. Speaker dissimilarity ratings for pairs of Mandarin and English time-reversed sentences. (A) Matrix of dissimilarity ratings averaged across all participants in both listener groups (NChinese Listeners = 20; NEnglish Listeners = 20): individual participants’ dissimilarity matrices are in a standardized arrangement, sothat rows and columns 1–20 (top and left) represent native voices (Mandarin for Chinese listeners, English for English listeners), whereas rows and columns
21–40 represent foreign language voices, regardless of listener group. The color scale indicates group-average dissimilarity ratings. (B) Average dissimilarity
ratings for the four different types of pairs. Cross-language pairs were rated as most dissimilar. Within the same-language pairs, crucially, native-language
pairs were rated as more dissimilar than foreign-language pairs, even though all sentences were unintelligible. (C) Listener × speaker interaction: bothparticipant groups record higher average dissimilarity ratings for native-language vs. foreign-language speaker pairs. Error bars indicate the SEM. All asterisks
denote P < 0.05.

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Discussion
We investigated whether the LFE in adults requires comprehension of the linguistic message. We found that listeners rated
pairs of speakers of their own language as more dissimilar on
average than pairs of speakers of a different language, eventhough all stimuli were rendered unintelligible by time-reversal.
This result implies that the LFE is not rooted in language
comprehension per se, but rather is based on familiarity with theacoustical fingerprint of one’s language, in a manner analogous
to the “Other-Race Effect” (ORE) in face recognition.
Participants were presented with pairs of time-reversed sentences spoken by different speakers and were asked to judge how
dissimilar the speakers were. Time-reversal was chosen because
it is a simple procedure that compromises intelligibility while
preserving some of the information present in the natural speech
signal. For example, time-reversal disrupts the temporal attributes of speech segments, such as onsets and decays, and reverses
pitch curves. Conversely, reversed speech is identical to natural
speech in amplitude, duration, and mean fundamental frequency. Furthermore, the formant transition structure of many
phonemes (e.g., fricatives and long vowels) is approximately
mirrored in the reversed signal, and important indexical cues to
speaker identity are also retained. In sum, this remaining information can enable high intersubject agreement in phoneme
transcription tasks (27), and can be used by the listener to aid
speaker recognition (28–30). Our participants were unable toextract any meaning from the stimuli, yet they showed reliable
differences in their identity dissimilarity ratings. The most salient
difference was between the different-language pairs (i.e., consisting of one sentence in English and one sentence in Mandarin)
and the same-language pairs: The listeners reliably rated pairs ofdifferent-language speakers as more dissimilar than pairs where
the language was consistent across identities (either bothspeakers in English or both in Mandarin), clearly visible in thedissimilarity matrix in Fig. 1A as red and green submatrices. This
result confirms that subjects were able to use acoustical information in the time-reversed sentences and were sensitive tooverall acoustical differences between the two languages (Table
S1 and SI Methods).
Crucially for our hypothesis, listeners also rated pairs of
speakers of their own language as more dissimilar than pairs of
speakers of the other language. The effect is highly significant andapparent as an interaction when ratings are split by speaker andlistener group in Fig. 1C. This effect is not driven by one subject
group, as there is no main effect of subject group on overall ratings
and the own-language effect is significant for each subject groupindividually. Nor is it explained by one of the sets of stimuli as theeffect of speaker language on the ratings was not significant either.
However, despite the absence of a main effect of listener group,
the native-language bias appears to be stronger in the English
listener group compared with the Chinese, reflected in the differences in effect sizes. This result may be explained by the factthat our Chinese participants had been resident in the United
Kingdom for 9 mo on average at the time of testing, and had
considerable functional experience with the English language. It
has been demonstrated, for example, that nonnative speaker
identification performance improves over several days’ worth oftraining (23).
Our results provide the first evidence, to our knowledge, of a LFE
in adult participants in the absence of speech comprehension. These
Fleming et al.

findings extend the results of Johnson et al. (26), who observed
a similar effect in 7-mo-old infants: In both cases, subjects had alimited understanding of the stimuli, yet they were more sensitive to
identity differences in native-language pairs compared with nonnative pairs. Interestingly, however, the infants in Johnson et al.’s(26) study did not show a discrimination bias for reversed native
speech compared with reversed foreign speech, as our adult listeners
did. The infants’ comparatively lower experience with the phonologyof their native language may account for this; specifically, whereas
7 mo of exposure may be sufficient to enable differentiation ofnative speakers uttering normal speech, it may be insufficient for thekind of fine-grained differentiation required under alien processing
conditions, as in the case of reversed speech. Indeed, even schoolaged children may not display adult-like performance in speakerrecognition tasks (37), suggesting that they cannot use the information available in an utterance as effectively as an adult listenerdespite their substantial experience of their native phonology andtheir greater exposure to different voices, compared with infants.
Therefore, it may be that infants do not yet possess the ability to
extract information from an unintelligible speech signal to aid
speaker discrimination and recognition, in ways that adults can, as
shown in our discrimination results and previous recognition results(28–30).
Thus, our findings refine Perrachione et al.’s (25) view that“human voice recognition depends on language ability” by supporting the notion that phonological processing is the key aspectof language ability which facilitates speaker individuation, butadding that comprehension of the spoken message is not necessary for such individuation. Their findings suggest that impaired voice recognition in dyslexics may be driven by theirknown deficits in phonological processing (24), whereas our
results show that the limited phonological information and indexical cues preserved after time-reversal are sufficient to allow
listeners to differentiate speakers. Moreover, extended exposure
with the particular distribution of acoustical features characteristic of their own language allowed our participants to perceptually “zoom-in” on speakers of that language, resulting in higher
native-language dissimilarity ratings, even when both native andnonnative speech content was unintelligible.
These findings draw an interesting parallel with an analogous
effect in another sensory modality: the ORE in face recognition.
The ORE is the well known phenomenon where observers are
typically poorer at discriminating and recognizing faces from adifferent racial group compared with their own (for a review, seeref. 38). One influential account of the ORE suggests that individual faces are represented as points in a multidimensional
space whose dimensions are shaped by perceptual experience andcode for diagnostic features (39, 40). Own-race faces, with which
an observer has more experience, become distributed more diffusely about the origin of the space (i.e., the average or prototypical face). Other-race faces, as a result of a different statistical
distribution of features, are encoded in a less efficient manner due
to a reliance on diagnostic dimensions for individuation optimized
for own-race faces. Other-race faces therefore mistakenly appear
more similar to one-another to the observer and this confusability
between faces underpins the impairment in other-race recognition
performance. Indeed, this model has found support at the
behavioral, computational (41) and neurophysiological levels(42–44). An analogous model could be invoked to account for our
results and those of Johnson et al. (26). One could conceive ofa similar “voice space” where voices are encoded as points basedon experience with indexical and linguistic attributes. Indeed,
the behavioral and physiological relevance of such a voice spacemodel has already been demonstrated (45). Speakers of one’s
native language would, in such a framework, be represented ina more distributed manner, resulting in higher interspeaker discriminability than for speakers of other languages to which thesubject has had less exposure and are therefore represented in
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difference for each group, and for all participants taken together.
We sampled participants’ difference scores with replacement(10,000 iterations) and derived separate confidence intervals foreach group (CI for Chinese participants: [0.007–0.04]; English:[0.005–0.07]; combined groups: [0.01–0.05]). As none of these
confidence intervals contained zero, the observed effects may beconsidered reliable.

a less differentiated, more compact manner. Such a model, while
acknowledging that comprehension can modulate speaker identification, would be consistent with the many noted similarities
between cerebral face and voice processing (46).
Methods
Participants. Twenty Mandarin-speaking Chinese (8 female, mean age = 23.7,
SD = 2.58) and 20 native English-speaking UK participants (10 female; mean
age = 24.25, SD = 3.01) were recruited. Chinese participants’ average durationof UK residency was 9.35 mo (SD = 7.34) and all had attained a minimum scoreof 6.5 on the IELTS test of English as a foreign language, or a comparable score
on an equivalent test. English-speaking participants reported no experience
with Mandarin Chinese. All participants were right-handed and reported nohistory of hearing difficulties or pathology. Participants gave written, informed consent for their involvement and received a monetary reward. The
experiment was approved by the Ethical Committee of the University ofGlasgow’s College of Science and Engineering.
Stimuli. Testing stimuli were drawn from a pool of 400 clips of 40 female
speakers (20 native English-speaking and 20 native Mandarin-speaking)reading 10 sentences (Open Speech Repository, 2005). Recordings weredigitized at a 16-bit/44.1-kHz sampling rate and cut into individual sentences.
Full, sentence-length stimuli were subsequently time-reversed, standardized
to duration of 1,250 ms (from original onset), and normalized for RMS
amplitude. The use of time-reversed clips of English and Mandarin speechensured that stimuli are of equal intelligibility to both participant groups

1. Assal G, Aubert C, Buttet J (1981) Asymétrie cérébrale et reconnaissance de la voix.
Rev Neurol (Paris) 137(4):255–268.
2. Van Lancker D, Kreiman J (1987) Voice discrimination and recognition are separate
abilities. Neuropsychologia 25(5):829–834.
3. Van Lancker DR, Cummings JL, Kreiman J, Dobkin BH (1988) Phonagnosia: A dissociation between familiar and unfamiliar voices. Cortex 24(2):195–209.
4. Van Lancker DR, Kreiman J, Cummings J (1989) Voice perception deficits: Neuroanatomical correlates of phonagnosia. J Clin Exp Neuropsychol 11(5):665–674.
5. Garrido L, et al. (2009) Developmental phonagnosia: A selective deficit of vocal
identity recognition. Neuropsychologia 47(1):123–131.
6. Belin P, Zatorre RJ, Lafaille P, Ahad P, Pike B (2000) Voice-selective areas in human
auditory cortex. Nature 403(6767):309–312.
7. Belin P, Zatorre RJ, Ahad P (2002) Human temporal-lobe response to vocal sounds.
Brain Res Cogn Brain Res 13(1):17–26.
8. Belin P, Zatorre RJ (2003) Adaptation to speaker’s voice in right anterior temporal
lobe. Neuroreport 14(16):2105–2109.
9. Poeppel D (2003) The analysis of speech in different temporal integration windows:
Cerebral lateralization as “asymmetric sampling in time”. Speech Commun 41:245–255.
10. von Kriegstein K, Eger E, Kleinschmidt A, Giraud AL (2003) Modulation of neural
responses to speech by directing attention to voices or verbal content. Brain Res Cogn
Brain Res 17(1):48–55.
11. Kriegstein KV, Giraud A-L (2004) Distinct functional substrates along the right superior temporal sulcus for the processing of voices. Neuroimage 22(2):948–955.
12. Poeppel D, Idsardi WJ, van Wassenhove V (2008) Speech perception at the interface ofneurobiology and linguistics. Philos Trans R Soc Lond B Biol Sci 363(1493):1071–1086.
13. McGettigan C, Scott SK (2012) Cortical asymmetries in speech perception: What’s
wrong, what’s right and what’s left? Trends Cogn Sci 16(5):269–276.
14. Martin CS, Mullennix JW, Pisoni DB, Summers WV (1989) Effects of talker variability
on recall of spoken word lists. J Exp Psychol Learn Mem Cogn 15(4):676–684.
15. Mullennix JW, Pisoni DB, Martin CS (1989) Some effects of talker variability on spoken
word recognition. J Acoust Soc Am 85(1):365–378.
16. Pisoni DB (1993) Long-term memory in speech perception: Some new findings ontalker variability, speaking rate and perceptual learning. Speech Commun 13(1-2):
109–125.
17. Nygaard LC, Pisoni DB (1998) Talker-specific learning in speech perception. Percept
Psychophys 60(3):355–376.
18. Thompson CP (1987) A language effect in voice identification. Appl Cogn Psychol 1:
121–131.
19. Goggin JP, Thompson CP, Strube G, Simental LR (1991) The role of language familiarity in voice identification. Mem Cognit 19(5):448–458.
20. Köster O, Schillert NO (1997) Different influences of the native language of a listeneron speaker recognition. Forensic Linguist. 4:18–28.
21. Winters SJ, Levi SV, Pisoni DB (2008) Identification and discrimination of bilingual
talkers across languages. J Acoust Soc Am 123(6):4524–4538.
22. Perrachione TK, Pierrehumbert JB, Wong PCM (2009) Differential neural contributions to native- and foreign-language talker identification. J Exp Psychol Hum
Percept Perform 35(6):1950–1960.
23. Perrachione TK, Wong PCM (2007) Learning to recognize speakers of a non-native
language: Implications for the functional organization of human auditory cortex.
Neuropsychologia 45(8):1899–1910.

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and therefore largely eliminated the influence of spoken language comprehension upon participants’ behavior. Stimuli were edited using Adobe
Audition 2 (Adobe Systems) and MATLAB 7.10 (R2010a).
Procedure. Testing took place within an anechoic cabin, where participants
were seated at a desktop PC. The experiment was programmed in MATLAB 7.5(R2007a). On every trial, participants heard a pair of voices and were instructed
to rate the likelihood that both voice clips had been produced by the same
speaker, using a visual analog scale where 0/far-left corresponded to “Same”
and 1/far-right corresponded to “Different.” Participants were advised to use
the full extent of the scale to record their responses and were permitted toreplay a trial as many times as they felt necessary before responding. This
procedure was repeated for all possible paired combinations of voice identity,
yielding a total of 820 pairs (40 × 39/2 + 40 same-identity pairs). The assignment of sentence to speaker was randomized across identities, ensuring thatno two voices in a pair ever produced the same sentence clip and that eachparticipant received a unique series of sentence-to-speaker pairings, in addition to a unique identity pairing order. The self-paced experiment took participants ∼2 h to complete, including an optional break when they hadreached trial 411 (i.e., halfway through the experiment). Participants had received previous exposure to the voice stimuli in this experiment through their
participation in an earlier functional Magnetic Resonance Imaging (fMRI) experiment, the results of which are not discussed here.
ACKNOWLEDGMENTS. P.B. was supported by the Biotechnology and Biological Sciences Research Council (BB/J003654/1).

24. Gabrieli JDE (2009) Dyslexia: A new synergy between education and cognitive neuroscience. Science 325(5938):280–283.
25. Perrachione TK, Del Tufo SN, Gabrieli JDE (2011) Human voice recognition depends
on language ability. Science 333(6042):595.
26. Johnson EK, Westrek E, Nazzi T, Cutler A (2011) Infant ability to tell voices apart rests
on language experience. Dev Sci 14(5):1002–1011.
27. Binder JR, et al. (2000) Human temporal lobe activation by speech and nonspeechsounds. Cereb Cortex 10(5):512–528.
28. Bricker PD, Pruzansky S (1966) Effects of stimulus content and duration on talker
identification. J Acoust Soc Am 40(6):1441–1449.
29. Van Lancker D, Kreiman J, Emmorey K (1985) Familiar voice recognition: Patterns andparameters. Part I: Recognition of backward voices. J Phonetics 13:19–38.
30. Sheffert SM, Pisoni DB, Fellowes JM, Remez RE (2002) Learning to recognize talkers
from natural, sinewave, and reversed speech samples. J Exp Psychol Hum Percept
Perform 28(6):1447–1469.
31. Eady SJ (1982) Differences in the F0 patterns of speech: Tone language versus stresslanguage. Lang Speech 25:29–42.
32. Mang EA (2001) Cross-language Comparison of Preschool Children’s Vocal Fundamental Frequency in Speech and Song Production. Res Stud Music Educ 16:4–14.
33. Keating P, Kuo G (2012) Comparison of speaking fundamental frequency in English
and Mandarin. J Acoust Soc Am 132(2):1050–1060.
34. Shu H, Anderson RC (1999) Learning to read Chinese: The development of metalinguistic
awareness. Reading Chinese Script: A Cognitive Analysis, eds Wang J, Inhoff A, Chen H(Lawrence Erlbaum Associates, Publishers, New Jersey).
35. Yeong SHM, Rickard Liow SJ (2012) Development of phonological awareness in English-Mandarin bilinguals: A comparison of English-L1 and Mandarin-L1 kindergarten
children. J Exp Child Psychol 112(2):111–126.
36. Duanmu S (2000) The Phonology of Standard Chinese (Oxford Press, New York).
37. Mann VA, Diamond R, Carey S (1979) Development of voice recognition: Parallels
with face recognition. J Exp Child Psychol 27(1):153–165.
38. Meissner CA, Brigham JC (2001) Thirty years of investigating the own-race bias inmemory for faces: A meta-analytic review. Psychol Public Policy Law 7:3–35.
39. Valentine T (1991) A unified account of the effects of distinctiveness, inversion, andrace in face recognition. Q J Exp Psychol A 43(2):161–204.
40. Valentine T, Endo M (1992) Towards an exemplar model of face processing: The effects of race and distinctiveness. Q J Exp Psychol A 44(4):671–703.
41. Caldara R, Abdi H (2006) Simulating the ‘other-race’ effect with autoassociative
neural networks: Further evidence in favor of the face-space model. Perception 35(5):659–670.
42. Vizioli L, Rousselet GA, Caldara R (2010) Neural repetition suppression to identity isabolished by other-race faces. Proc Natl Acad Sci USA 107(46):20081–20086.
43. Vizioli L (2012) Clarifying the neurophysiological basis of the other-race effect. Ph. D.
Thesis (University of Glasgow, Glasgow, United Kingdom).
44. Brosch T, Bar-David E, Phelps EA (2013) Implicit race bias decreases the similarity ofneural representations of black and white faces. Psychol Sci 24(2):160–166.
45. Latinus M, McAleer P, Bestelmeyer PEG, Belin P (2013) Norm-based coding of voice
identity in human auditory cortex. Curr Biol 23(12):1075–1080.
46. Yovel G, Belin P (2013) A unified coding strategy for processing faces and voices.
Trends Cogn Sci 17(6):263–271.

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