Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction.

Nass, Clifford; Lee, Kwan Min

doi:10.1037/1076-898x.7.3.171

Cited by 351 publications

(314 citation statements)

References 42 publications

Supporting

Mentioning

294

Contrasting

Unclassified

Order By: Relevance

“…The feature labels are in Table 6. For example, the model of extraversion in Table 23 confirms previous findings by associating this trait with longer conversations (Rule 5), a high speech rate (Rules 1 and 4) and a high pitch (Rules 2, 6 and 7) (Nass & Lee, 2001). But new markers emerge, such as a high pitch variation for introverts (Rules 15,18 and 20), contradicting previous findings reported by Scherer (1979).…”

Section: Qualitative Analysiscontrasting

confidence: 55%

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Mairesse¹,

Walker²,

Mehl³

et al. 2007

jair

743

583

View full text Add to dashboard Cite

It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content. One such type of information consists of cues to the speaker's personality traits, the most fundamental dimension of variation between humans. Recent work explores the automatic detection of other types of pragmatic variation in text and conversation, such as emotion, deception, speaker charisma, dominance, point of view, subjectivity, opinion and sentiment. Personality affects these other aspects of linguistic production, and thus personality recognition may be useful for these tasks, in addition to many other potential applications. However, to date, there is little work on the automatic recognition of personality traits. This article reports experimental results for recognition of all Big Five personality traits, in both conversation and text, utilising both self and observer ratings of personality. While other work reports classification results, we experiment with classification, regression and ranking models. For each model, we analyse the effect of different feature sets on accuracy. Results show that for some traits, any type of statistical model performs significantly better than the baseline, but ranking models perform best overall. We also present an experiment suggesting that ranking models are more accurate than multi-class classifiers for modelling personality. In addition, recognition models trained on observed personality perform better than models trained using selfreports, and the optimal feature set depends on the personality trait. A qualitative analysis of the learned models confirms previous findings linking language and personality, while revealing many new linguistic markers.

show abstract

Section: Qualitative Analysiscontrasting

confidence: 55%

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Mairesse¹,

Walker²,

Mehl³

et al. 2007

jair

743

583

View full text Add to dashboard Cite

show abstract

“…Therefore, speech data should allow one to perform APP reasonably well. While still being limited, the results proposed so far in the speech literature seem to confirm the indications above for both APR (Mairesse et al, 2007;Ivanov et al, 2011) and APP (Mairesse et al, 2007;Mohammadi and Vinciarelli, 2012;Polzehl et al, 2010;Valente et al, 2012;Nass and Min Lee, 2001;Schmitz et al, 2007;Trouvain et al, 2006).…”

Section: Speaker Personalitysupporting

confidence: 70%

“…The APP studies presented so far deal with real voices, but the literature shows that human listeners tend to assign personality traits to artificial speakers as well (Nass and Min Lee, 2001;Schmitz et al, 2007;Trouvain et al, 2006). Nass and Min Lee (2001) pioneered the manipulation of prosody (intensity, pitch, pitch range, and speaking rate) aiming at producing speech that sounds more or less extroverted.…”

Section: Speaker Personalitymentioning

confidence: 99%

“…Nass and Min Lee (2001) pioneered the manipulation of prosody (intensity, pitch, pitch range, and speaking rate) aiming at producing speech that sounds more or less extroverted. Their experiments showed not only that the personality ratings assigned by human judges are consistent with the manipulation of prosody, but also that listeners tend to prefer artificial voices that they perceive as being closer to themselves in terms of personality.…”

Section: Speaker Personalitymentioning

confidence: 99%

See 1 more Smart Citation

A Survey on perceived speaker traits: Personality, likability, pathology, and the first challenge

Schuller

Steidl

Batliner

et al. 2015

Computer Speech & Language

View full text Add to dashboard Cite

The INTERSPEECH 2012 Speaker Trait Challenge aimed at a unified test-bed for perceived speaker traits -the first challenge of this kind: personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In the present article, we give a brief overview of the state-of-the-art in these three fields of research and describe the three sub-challenges in terms of the challenge conditions, the baseline results provided by the organisers, and a new openSMILE feature set, which has been used for computing the baselines and which has been provided to the participants. Furthermore, we summarise the approaches and the results presented by the participants to show the various techniques that are currently applied to solve these classification tasks.

show abstract

“…The similarityattraction principle, which assumes that individuals are more attracted to others who manifest the same personality as themselves, has been studied in HCI (e.g., [25]). We designed two interaction scripts, one that displayed an extroverted and challenging personality type and one that displayed an introverted and nurturing personality type, expressed through the choice of words and paralinguistic cues.…”

Section: B Verbal and Para-verbal Communicationmentioning

confidence: 99%

User Personality Matching with a Hands-Off Robot for Post-stroke Rehabilitation Therapy

Tapus

Matarić

Experimental Robotics

View full text Add to dashboard Cite

Abstract. This paper describes a hands-off therapist robot that monitors, assists, encourages, and socially interacts with post-stroke users engaged in rehabilitation exercises. We investigate the role of the robot's personality in the hands-off therapy process, focusing on the relationship between the level of extroversion-introversion of the robot and the user. The experiments map the extraversion-introversion personality dimension and the spectrum of therapy styles that range from challenging to nurturing. The experimental results provide first evidence for the preference of personality matching in the assistive domain.

show abstract

Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction.

Cited by 351 publications

References 42 publications

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

A Survey on perceived speaker traits: Personality, likability, pathology, and the first challenge

User Personality Matching with a Hands-Off Robot for Post-stroke Rehabilitation Therapy

Contact Info

Product

Resources

About