Introduction

When we see a new face or hear a new voice, we automatically form a first impression of the person’s personality, inferring traits such as trustworthiness, competence, attractiveness, etc. [1–5]. These personality impressions may not be accurate but they are robust, showing high inter-rater agreement [1, 3, 4, 6]; they also form very rapidly as sub-second exposure to face or voice is sufficient to yield consistent judgments [4, 7].

For both faces and voices, first impressions are well summarized by a 2-dimensional « social space » with axes mapping onto perceived trustworthiness and dominance, respectively [4, 6]. These two dimensions are thought to reflect evolutionary ancient mechanisms, developed under selective pressures of living in large social groups, to rapidly assess the dispositions (trustworthiness) and ability to act on these dispositions (dominance) of an unfamiliar conspecific individual [8, 9].

For faces, data-driven computational modelling has demonstrated a very tight relationship between perceived personality impressions and specific facial features [1, 10]. The generation of model faces at different positions of the “social face space” shows that the main dimensions of trustworthiness and dominance map onto very different sets of facial features, such as jaw shape vs. eyebrow shape.

For voices, significant correlations between personality ratings and acoustical parameters such as average fundamental frequency (f0) have been observed [2, 4–6, 11, 12] suggesting an acoustical basis for voice personality judgments by which principled changes in voice acoustics could potentially map onto desired changes in perceived personality. But until now the correlational approach has fallen short of precisely characterizing this acoustical basis. How exactly should one say “hello” to be perceived as e.g., trustworthy by new listeners?

Here we used voice computational modelling to investigate more closely the acoustical basis for perceived voice personality. We focused on perceived trustworthiness because due to its evolutionary link to cooperation and survival, trust is a central aspect of human communication, the foundation for functioning relationships, interpersonal interaction, effective collaboration [13] as well as the cooperation and mutual adaptation needed to surmount problems [14, 15]. Ratings along that trait were found in a previous study to correlate most strongly with the first principal component explaining more than half the variance in overall voice personality ratings [4], indicating the foremost role of perceived trustworthiness in overall voice personality impressions.

We first asked whether voices modeled as the acoustical average of several low- and high-trustworthy voices would also be rated with low and high perceived trustworthiness—potential evidence of a mapping between acoustical and social voice spaces. We further explored the possible parametric nature of the acoustics-personality link by collecting trustworthiness ratings for voice stimuli generated in voice space along the direction defined by the low- and high-trustworthiness prototypes, including caricatures of one relative to the other.

Thirty-two male voices saying the word “hello” were ranked by their average trustworthiness ratings collected as part of a previous study [4] (Fig 1A). A low- and a high-trustworthiness prototypical model voice stimuli were generated via high-quality analysis/resynthesis [16] based on the average acoustical characteristics of the eight voices (25%) rated lowest and highest on perceived trustworthiness, respectively (Fig 1B; S1 Fig; cf. Methods). This resulted in two natural-sounding “hellos” (cf. audio files in Supporting Information) with perceptually different voice qualities and which we hypothesized would yield different trustworthiness ratings.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 1. Generation of the voice trustworthiness continuum.

a. Thirty-two male voices saying “hello” are represented as points in a 2D “social voice space” with axes mapping onto their perceived Trustworthiness and Dominance, respectively (cf. [4]). Coloured dots indicate voices within the bottom (blue dots) and top (red dots) 25% for Trustworthiness ratings. b. A Low- and a High-Trustworthiness prototype were generated by averaging via morphing the bottom and top 25% rated voices, respectively. c. A 9-stimulus “Trustworthiness continuum “was generated via morphing in 8 acoustically equal steps using the two prototypes in intermediate positions and extrapolating to low- and high- Trustworthiness caricatures at extreme positions.

https://doi.org/10.1371/journal.pone.0185651.g001

Next we generated via morphing a 9-stimulus “Trustworthiness continuum” in acoustically equal steps that contained the low- and high-trustworthiness prototypes at intermediate positions (stimuli S3 and S7) and that inter- and extrapolated their acoustical difference resulting at the extreme in low- and high-trustworthiness caricatures (stimuli S1 and S9; Fig 1C). To provide a set of control stimuli generated similarly but unrelated to trustworthiness we generated a second 9-stimulus “Control continuum” based on a random selection of a set of 32 female voices (S2 Fig; audio files in Supporting Information).

Five hundred listeners (146 males, 354 females; aged 19–65 years old, median age = 24) each provided 18 trustworthiness ratings using a visual analogue scale via an online interface, 9 for the morphed voice stimuli of the Trustworthiness continuum and 9 for those of the control continuum (stimuli presented in random orders in counterbalanced blocks; cf. Methods). Ratings were z-scored within participant prior to analysis to account for inter-individual variation in rating scale use and because we were primarily interested in rating variations within the morphed continua rather than in their absolute values.

Results and discussion

We observed a compelling relationship between average trustworthiness rating z-scores and position along the Trustworthiness, but not the Control, continuum (Fig 2). Ratings for the low- and high- trustworthiness prototypes (stimuli S3 and S7) differed highly significantly with lower average ratings for S3 than S7 (S3: z = -0.39; S7: z = 0.40; paired t-test: t(499) = 14.2, p = 1.38e-38) while no such difference was apparent for control stimuli (S3: z = 0.03; S7:z = -0.09; t(499) = 2.08, p = 0.038 n.s. after Bonferroni correction).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 2. Acoustic-based modulation of perceived voice trustworthiness.

Filled circles indicate group-average Trustworthiness rating z-scores for each of the 9 stimuli of the Trustworthiness continuum (spectrograms at bottom) from low-Trustworthiness caricature (S1) to low- Trustworthiness prototype (S3; blue rectangle), to the average of the two prototypes (S5), to the high-Trustworthiness prototype (S7; red rectangle) to the high- Trustworthiness caricature (S9). Note the quasi-linear modulation of Trustworthiness ratings along the Trustworthiness continuum. Open circles show average T-rating z-scores for the control continuum. Error bars and shaded areas indicate mean ± s.e.m.

https://doi.org/10.1371/journal.pone.0185651.g002

We also observed a strong caricaturing effect, as exaggerating acoustical differences between S3 and S7 yielded higher average trustworthiness ratings for S9 than S7 (S7: z = 0.40; S9; z = 0.80; t(499) = 7.40, p = 5.93e-13), and lower average trustworthiness ratings for S1 than for S3 (S1: z = -0.67; S3: z = -0.38; t(499) = 6.06; p = 2.62e-9). In fact, the average ratings followed an extremely tight relation with acoustical differences over the whole range of the continuum generated resulting in a correlation of r = 0.99 (p = 5.03e-8) between position along the acoustical continuum and average ratings. Again no such effect was observed for control stimuli on either end of the continuum (S1: z = 0; S3: z = 0.03; t(499) = -0.49, p = 0.62 n.s.; S7: z = -0.09; S9: z = -0.19; t(499) = 1.63, p = 0.10 n.s.). Importantly these effects were highly similar for male and female listeners (S3 Fig) suggesting the involvement of gender-independent mechanisms.

These results demonstrate a strong acoustical basis for perceived voice personality—an extremely tight mapping between acoustical and social voice spaces. Not only does averaging voices from different individuals with low or high ratings trustworthiness ratings results in the predicted rating differences, but these personality impressions can be parametrically modulated, with unsuspected accuracy, by acoustical changes and considerably increased (or decreased) by caricaturing.

Furthermore the results allow listening to the prototypes and caricatures, permitting direct access to the listeners’ underlying representations of voice trustworthiness in a way that correlations between traits and acoustical measures could not allow. The comparison of the prototypes and caricatures’ acoustical measures not only gives insight on which acoustical parameters are relevant, it directly shows how to modulate these parameters in order to achieve the desired increase in perceived trustworthiness.

Thus, past studies have hinted at a relation between f0 variation in voice and perceived personality traits such as attractiveness [5, 11, 17] or trustworthiness [4] but they could not determine the exact f0 contour that would drive this percept. What intonation should one use when saying “hello” to be perceived as trustworthy? Fig 3 provides important elements of answer to that question by illustrating f0 contour for the 9 stimuli of the Trustworthiness continuum. F0 at mid-duration is roughly similar for all stimuli but a clear difference is apparent for starting and ending f0: while the low-trustworthiness stimuli started with a relatively low f0 remaining flat or slightly increasing over the course of the syllables, the high-trustworthiness stimuli started with a much higher f0 at first syllable then decreased markedly to reach the average f0 at mid utterance and finish on a strongly rising intonation (cf. High trustworthiness caricature, Fig 3, audio file in Supporting Information). While f0 contour is but one of the many only acoustical parameters varying between stimuli (S1 Table) this particular intonation likely plays a central role on yielding the observed change in perceived personality impressions. Further work should weigh the respective importance of the different parameters by selectively including them or not in the prototypical models, for instance by generating an acoustic continuum that only manipulates f0 between the different stimuli, all other parameters being kept constant.

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Fig 3. Intonation and perceived trustworthiness.

The f0 contours of the 9 stimuli of the Trustworthiness continuum (from light blue for stimulus S1 to red for stimulus S9) are shown illustrating a marked change in intonation as perceived trustworthiness increases. While low trustworthiness stimuli have a flat or slightly rising f0 contour, high-trustworthiness stimuli are characterized by a marked f0 drop at the end of the first syllable to finish the second syllable on a markedly rising f0.

https://doi.org/10.1371/journal.pone.0185651.g003

The results could potentially generalize to other personality traits, providing unprecedented insight into the voice acoustics that drive the perception of different traits and opening numerous possibilities for research. They also open the door to practical applications where desired personality impressions need to be achieved. Indeed, there are many situations in life where voice is the only cue available to form a new person’s first impression and where that first impression strongly influences future interactions. For example, telephone interviewers face the task of establishing trust a creating a positive impression with only vocal cues. Since the great majority of refusals occur within the opening phrases [18], a positive attitude towards both the subject and interviewer could be induced early on via acoustical modulation, resulting in increased response rates. Furthermore, speech synthesis and computer-generated voices foe e.g. social robots could be addressed as a promising area for applications in the customer service sector.

Some limitations of the study should be mentioned: They were only obtained on the word “hello”, which is justified as this word is often associated with first encounters, yet it is unclear how they would generalize other words or longer utterances. Moreover, the 8-voices averages are probably but a crude approximation of the listeners’ internal representations of trustworthy and untrustworthy voices; results could likely be further enhanced by using high-level reverse correlation for a more precise estimation of the listeners’ internal templates [19, 20]. Our choice of female voices for the control condition and male voice for the experimental condition was entirely arbitrary; it will be important in future studies to examine the potential role of factors such as gender, but also age, ethnicity, accent, verbal content, etc. on the acoustical basis for voice personality impressions.

Methods

Supporting information