Skip to main content
SpringerLink
Log in

Speech-Based Personality Assessment

  • Chapter
  • First Online:
Personality in Speech

Part of the book series: T-Labs Series in Telecommunication Services ((TLABS))

  • 1488 Accesses

Abstract

As the previous chapter outlined approaches to personality assessment in psychology, this chapter summarizes works and insights of researchers from the speech community. Many of these researchers are linguists or computer scientists, hence the aim of approaching an individual’s personality translates into the aim of modeling or experimenting with personality. Essentially, the assessment of perceivable manifestations of personality is the basis for any experimentation. When analyzing personality in terms of speech, the scope of interest is narrowed down from overall personality, i.e., maybe being able to judge about personality from previous knowledge about actions or incidents, towards focusing on perceivable characteristics, in this respect it means perceivable at the very point in time the conversation or the experiment occurs as well as comprehensible to any person including persons having no prior knowledge about the speaker. Resulting limitations and cleavages of this respective will be addressed in the present chapter.

Voices are not merely a handy means to transmit information to the user. All voices—natural, recorded, or synthetic—activate automatic judgments about personality.

Nass et al. (1995)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The terms “outer” and “inner” were chosen by the authors.

  2. 2.

    Kreiman’s categories are basis for the current work, which extends the proposed categories.

  3. 3.

    The term translates to sonorous, pompous.

  4. 4.

    Note that the actual correct reference to pitch in term of speech synthesis is the acoustic correlate of the perceived pitch. i.e., F0. For simplification and comparability in the literature review, the term pitch is retained throughout this chapter. For details on how to obtain acoustic measurements for the perceived pitch and respective terminology please refer to Sect. 5.3.2.

  5. 5.

    In his experiment Apple re-synthesized recordings after altering the speech using the LPC method of Atal and Hanauer (1971). LPC abbreviates linear predictive coding and is one out of many methods in speech synthesis.

  6. 6.

    More details on measurements are given in Sect. 5.7. As for now, the F-measure can be seen as accuracy-related measure accounting for a single class out of a multi-class classification task which is less biased by class distribution imbalance. The value of \(0.8\) corresponds to good classification success.

References

  • Addington DW (1968) The relationship of selected vocal characteristics to personality perceptions. Speech Monogr 35(4):492–503

    Article  Google Scholar 

  • Allport GW, Cantril H (1934) Judging personality from voice. J Soc Psychol 5(1):37–55

    Article  Google Scholar 

  • Apple W, Streeter LA, Krauss RM (1979) Effects of pitch and speech rate on personal attributions. J Personality Soc Psychol 37(5):715–727

    Article  Google Scholar 

  • Aronovitch CD (1976) The voice of personality: stereotyped judgments and their relation to voice quality and sex of speaker. J Soc Psychol 99(2):207–220

    Article  Google Scholar 

  • Atal BS, Hanauer SL (1971) Speech analysis and synthesis by linear prediction of the speech wave. J Acoust Soc Am 50(2B):637–655

    Article  Google Scholar 

  • Ball D, Hill L, Freeman B, Eley TC, Strelau J, Riemann R, Spinath FM, Angleitner A, Plomin R (1997) The serotonin transporter gene and peer-rated neuroticism. NeuroReport 8(5):1301–1304

    Article  Google Scholar 

  • Berry DS (1990) Vocal attractiveness and vocal babyishness: effects on stranger, self, and friend impressions. J Nonverbal Behav 14(3):141–153

    Article  Google Scholar 

  • van Bezooijen R (1995) Sociocultural aspects of pitch differences between japanese and dutch women. Lang Speech 38:253–265

    Google Scholar 

  • Bickmore T, Cassell J (2004) Natural intelligent and effective interaction with multimodal dialogue systems. Kluwer Academic, New York

    Google Scholar 

  • Breese J, Ball G (1998) Modeling emotional state and personality for conversational agents. Technical Report MSR-TR-98-41, Microsoft Research

    Google Scholar 

  • Brown B, Strong W, Rencher A (1974) Fifty-four voices from two: the effects of simultaneous manipulations of rate, mean fundamental frequency, and variance of fundamental frequency on ratings of personality from speech. J Acoust Soc Am 55:313–318

    Article  Google Scholar 

  • Buller DB, Aune RK (1988) The effects of vocalics and nonverbal sensitivity on compliance a speech accommodation theory explanation. Hum Commun Res 14:548–568

    Article  Google Scholar 

  • Burkhardt F, Ballegooy Mv, Engelbrecht K-P, Polzehl T, Stegmann J (2009a) Emotion detection in dialog systems: applications, strategies and challenges. In: Proceedings of international conference on affective computing and intelligent interaction (ACII (2009)) vol 1. IEEE Netherlands, Amsterdam

    Google Scholar 

  • Burkhardt F, Polzehl T, Stegmann J, Metze F, Huber R (2009b) Detecting real life anger. In: Proceedings of international conference on acoustics, speech, and signal processing (ICASSP (2009)) vol 1. Taipei, Taiwan, IEEE, pp 4761–4764

    Google Scholar 

  • Cantril H, Allport G (1935) The psychology of radio. Harper and Brothers, New York

    Google Scholar 

  • Cassell J, Sullivan J, Prevost S, Churchill E (eds) (2000) Embodied conversational agents. The MIT Press, Cambridge

    Google Scholar 

  • Cassell J, Bickmore T (2003) Negotiated collusion: modeling social language and its relationship effects in intelligent agents. User Model User Adapt Interact 13(1–2):89–132

    Article  Google Scholar 

  • Catrambone R, Stasko J, Xiao J (2002) Anthropomorphic agents as a user interface paradigm: experimental findings and a framework for research. In: 24th annual conference of the cognitive science society, pp 166–171

    Google Scholar 

  • Chen Y, Naveed A, Porzel R (2010) Behavior and preference in minimal personality: a study on embodied conversational agents. In: International conference on multimodal interfaces and the workshop on machine learning for multimodal interaction, ICMI-MLMI’ 10, 49:1–49:4, New York, NY, USA. ACM

    Google Scholar 

  • Dix A, Finlay J, Abowd G, Beale R (2004) Human-computer interaction, 3rd edn. Prentice-Hall, Upper Saddle River

    Google Scholar 

  • Enos F, Benus S, Cautin RL, Graciarena M, Hirschberg J, Shriberg E, (2006) Personality factors in human deception detection: comparing human to machine performance, ISCA, pp 813–816

    Google Scholar 

  • Eyben F, Wöllmer M, Schuller B (2010) OpenSMILE—The Munich versatile and fast open-source audio feature extractor, 1459. ACM Press, New York

    Google Scholar 

  • Gill AJ, French RM (2007) Level of representation and semantic distance: rating author personality from texts. In: Proceedings of the second european cognitive science conference (EuroCogsci07), Delphi, Greece

    Google Scholar 

  • Gosling S (2003) A very brief measure of the Big-Five personality domains. J Res Pers 37(6):504–528

    Article  Google Scholar 

  • Hunt RG, Lin TK (1967) Accuracy of judgments of personal attributes from speech. J Personality Soc Psychol 6(4):450–453

    Article  Google Scholar 

  • Ivanov AV, Riccardi G, Sporka AJ, Franc J (2011) Recognition of personality traits from human spoken conversations. Most (August) pp 1549–1552

    Google Scholar 

  • John OP, Srivastava S (1999) The Big Five trait taxonomy: history, measurement, and theoretical perspectives. J Personality 2(2):102–138

    Google Scholar 

  • Kreiman J, Sidtis D (2011) Foundations of voice studies, an interdisciplinary approach to voice production and perception. Wiley-Blackwell, West Sussex

    Book  Google Scholar 

  • Mairesse F, Walker MA, Mehl MR, Moore RK (2007) Using linguistic cues for the automatic recognition of personality in conversation and text. J Artif Intell Res 30:457–500

    MATH  Google Scholar 

  • Mairesse F, Walker M (2007) PERSONAGE: personality generation for dialogue, Association for computational linguistics

    Google Scholar 

  • Mairesse F, Walker MA (2011) Controlling user perceptions of linguistic style: trainable generation of personality traits. Comput Linguistics 37(January 2009):1–34

    Google Scholar 

  • Mallory EB, Miller VR (1958) A possible basis for the association of voice characteristics and personality traits. Speech Monogr 25:255–260

    Article  Google Scholar 

  • Mehl MR, Pennebaker JW, Crow DM, Dabbs J, Price JH (2001) The electronically activated recorder (EAR): a device for sampling naturalistic daily activities and conversations. Behavior Res Methods Instrum Comput J Psychon Soc Inc 33(4):517–523

    Article  Google Scholar 

  • Mehl MR, Gosling SD, Pennebaker JW (2006) Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life. J Person Soc Psychol 90(5):862–877

    Article  Google Scholar 

  • Metze F, Batliner A, Eyben F, Polzehl T, Schuller B, Steidl S (2010) Emotion recognition using imperfect speech recognition. In: Proceedings of the annual conference of the international speech communication association (Interspeech 2009), Makuhari, Japan, IEEE, pp 1–6

    Google Scholar 

  • Metze F, Black A, Polzehl T (2011) A review of personality in voice-based man machine interaction. In: Human-Computer Interaction. Interaction techniques and environments—14th international conference, HCI International 2011, Springer, pp 358–367

    Google Scholar 

  • Metze F, Polzehl T, Wagner M (2009) Fusion of acoustic and linguistic speech features for emotion detection. In: Proceedings of international conference on semantic computing (ICSC 2009) vol 1. Berleley, USA, CA, IEEE

    Google Scholar 

  • Miller N, Maruyama G, Beaber RJ, Valone K (1976) Speed of speech and persuasion. J Pers Soc Psychol 34(4):615624

    Article  Google Scholar 

  • Mohammadi G, Mortillaro M, Vinciarelli A (2010) The voice of personality: mapping nonverbal vocal behavior into trait attributions. In: Proceedings of the international workshop on social signal processing, pp 17–20

    Google Scholar 

  • Mohammadi G, Vinciarelli A (2011) Humans as feature extractors: combining prosody and personality perception for improved speaking style recognition. In: Proceedings of IEEE international conference on systems, man and cybernetics, pp 363–366

    Google Scholar 

  • Moore W (1939) Personality traits and voice quality deficiencies. J Speech Hear Disord 4:33–36

    Article  Google Scholar 

  • Nass C, Moon Y, Fogg B, Reeves B, Dryer DC (1995) Can computer personalities be human personalities? Int J Hum Comput Stud 43:223–239

    Article  Google Scholar 

  • Nass C, Brave S (2005) Wired for speech: how voice activates and advances the human-computer relationship. The MIT Press, Cambridge

    Google Scholar 

  • Nass C, Lee KM (2001) Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction. J Exp Psychol, pp 171–181

    Google Scholar 

  • Oberlander J, Gill A (2004) Individual differences and implicit language: personality, parts-of-speech and pervasiveness. In: Cognitive Science Society, Chicago, IL, USA

    Google Scholar 

  • Paeschke A (2003) Prosodische analyse emotionaler sprechweise—dissertation TU berlin, vol 1. Reihe Mündliche Kommunikation. Logos Verlag, Berlin

    Google Scholar 

  • Pear T (1931) Voice and personality. Chapman & Hall, London

    Google Scholar 

  • Peng Y, Zebrowitz L, Lee H (1993) The impact of cultural background and cross-cultural experience on impressions of american and korean male speakers. J Cross-Cultural Psychol 24(2):203–220

    Article  Google Scholar 

  • Pianesi F, Mana N, Cappelletti A, Lepri B, Zancanaro M (2008) Multimodal recognition of personality traits in social interactions. In: Proceedings of the 10th international conference on multimodal interfaces IMCI 08, 53

    Google Scholar 

  • Pierrehumbert J (1979) The perception of fudamental frequency declination. J Acoust Soc Am 66:363369

    Article  Google Scholar 

  • Pittam J (1994) Voice in social interaction: an interdisciplinary approach. Sage Publications Inc., Baldwin City

    Google Scholar 

  • Polzehl T (2006) Automatische klassifizierung emotionaler sprechweisen. In: Tagungsband 1.Kongress Multimediatechnik, Wismar, Germany

    Google Scholar 

  • Polzehl T, Metze F (2008) Using prosodic features to prioritize voice messages. In: SIGIR, Singapore proceedings of speech search workshop at SIGIR

    Google Scholar 

  • Polzehl T, Schmitt A, Metze F (2009a) Comparing features for acoustic anger classification in german and english IVR systems. In: Proceedings of international workshop of spoken dialogue systems (IWsDs 2009) vol 1. University of Ulm, Germany, Ulm

    Google Scholar 

  • Polzehl T, Sundaram S, Ketabdar H, Wagner M, Metze F (2009b) Emotion classification in children’s speech using fusion of acoustic and linguistic features. In: Proceedings of the annual conference of the international speech communication association (Interspeech 2009), Brighton, England. ISCA, pp 340–343

    Google Scholar 

  • Polzehl T, Metze F, Schmitt A (2010a) Linguistic and prosodic emotion recognition. Deutsche Jahrestagung für Akustik (DAGA). DAGA, DAGA, pp 1–2

    Google Scholar 

  • Polzehl T, Möller S, Metze F (2010b) Automatically assessing acoustic manifestations of personality in speech. In: Workshop on spoken language technology, Berkeley, USA IEEE

    Google Scholar 

  • Polzehl T, Möller S, Metze F (2010c) Automatically assessing personality from speech. In: Proceedings of international conference on semantic computing (ICSC 2010), IEEE, pp 1–6

    Google Scholar 

  • Polzehl T, Schmitt A, Metze F (2010d) Approaching multi-lingual emotion recognition from speech—on language dependency of acoustic/prosodic features for anger detection. In: SpeechProsody, Chicago, IL, USA. University of Illionois, pp 1–6

    Google Scholar 

  • Polzehl T, Schmitt A, Metze F (2010e) Salient Features for Anger Recognition in German and English IVR Portals. In: Spoken dialogue systems technology and design. Springer, Berlin, Germany, pp 81–110

    Google Scholar 

  • Polzehl T, Möller S, Metze F (2011a) Modeling speaker personality using voice. In: Proceedings of the annual conference of the international speech communication association (Interspeech 2011), Florence, Italy. ISCA

    Google Scholar 

  • Polzehl T, Schmitt A, Metze F, Wagner M (2011b) Anger recognition in speech using acoustic and linguistic cues. Speech communication, special issue: sensing emotion and affect—facing realism in speech processing

    Google Scholar 

  • Polzehl T, Schoenenberg K, Möller S, Metze F, Mohammadi G, Vinciarelli A (2012) On speaker-independent personality perception and prediction from speech. In: Proceedings of INTERSPEECH 2012

    Google Scholar 

  • Rammstedt B, John O (2007) Measuring personality in one minute or less: a 10-item short version of the big five inventory in english and german. J Res Pers 41(1):203–212

    Article  Google Scholar 

  • Reeves B, Nass C (1996) The media equation: how people treat computers, television, and new media like real people and places. Cambridge University Press, Cambridge

    Google Scholar 

  • Sanford FH (1942) Speech and personality: a comparative case study. J Personality 10:169198

    Article  Google Scholar 

  • Schapire RE, Singer Y (2000) BoosTexter: a boosting-based system for text categorization. In: Machine Learning, 135–168

    Google Scholar 

  • Scherer KR (1974) Voice quality analysis of american and german speakers. J Psycholinguistic Res 3:281–298. doi:10.1007/BF01069244

  • Scherer KR (1979) Personality markers in speech, Cambridge University Press, Cambridge, pp 147–209

    Google Scholar 

  • Scherer KR, Scherer U (1981) Speech behavior and personality. Speech Evaluation Psychiatry, 115–135

    Google Scholar 

  • Scherer KR (1977) Effect of stress on fundamental frequency of the voice. J Acoust Soc Am 62:25–26

    Article  Google Scholar 

  • Schmitt A, Pieraccini R, Polzehl T (2010a) For heavens sake, gimme a live person! designing emotion-detection customer care voice applications in automated call centers. Advances in speech recognition. Springer, US, Berlin, Germany, pp 81–110

    Google Scholar 

  • Schmitt A, Polzehl T, Minker W (2010b) Facing reality: simulating deployment of anger—recognition in IVR systems. In: Spoken dialogue systems for ambient environments—lecture notes in computer science, vol V. 6392, Springer, Makuhari, Japan, pp 23–48

    Google Scholar 

  • Schmitt A, Polzehl T, Minker W, Liscombe J (2010c) The influence of the utterance length on the recognition of aged voices. In Calzolari N, Choukri K, Maegaard B, Mariani JOJ, Piperidis S, Rosner M, Tapias D (eds) Proceedings of the seventh conference on international language resources and evaluation (LREC’10), Valletta, Malta. European Language Resources Association (ELRA), pp 1–6

    Google Scholar 

  • Schmitt A, Polzehl T, Minker W (2010d) Modeling a-priori likelihoods for angry user turns with hidden markov models. In: SpeechProsody, Chicago, IL., USA. University of Illionoise, pp 1–6

    Google Scholar 

  • Schröder M, Trouvain J (2003) The german text-to-speech synthesis system mary: a tool for research, development and teaching. Int J Speech Technol 6(4):365–377

    Article  Google Scholar 

  • Schröder M, Grice M (2003) Expressing vocal effort in concatenative synthesis, 25892592. Citeseer

    Google Scholar 

  • Schuller B, Metze F, Steidl S, Batliner A, Eyben F, Polzehl T (2009) Late fusion of individual engines for imrpoved recognition of negative emotion in speech—learning vs. democratic vote. In: International conference on acoustics, speech and signal processing (ICASSP). IEEE

    Google Scholar 

  • Smith R, Parker E, Noble E (1975) Alcohol’s effect on some formal aspects of verbal social communication. Archives Gen Psychiatry 32(11):1394–1398

    Article  Google Scholar 

  • Stagner R (1936) Judgments of voice and personality. J Educational Psychol 27(4):272–277

    Article  Google Scholar 

  • Taylor HC (1934) Social agreement on personality traits as judged from speech. J Soc Psychol 5:244–248

    Article  Google Scholar 

  • Trouvain J, Schmidt S, Schröder M, Schmitz M, Barry WJ (2006) Modelling personality features by changing prosody in synthetic speech. Number Table 2. ISCA, pp 4–7

    Google Scholar 

  • Tsalikis J, DeShields OJ, LaTour M (1991) The role of accent on the credibility and effectiveness of the salesman. J Pers Sell Sales Management 11:31–41

    Google Scholar 

  • Tusing K (2000) The sounds of dominance. vocal precursors of perceived dominance during interpersonal influence. Hum Commun Res 26(1):148–171

    Google Scholar 

  • Walker MA, Cahn JE, Whittaker SJ (1997) Improvising linguistic style: social and affective bases for agent personality. In: Proceedings of autonomous agents, p 10

    Google Scholar 

  • Winkler R (2003) Merkmale junger und alter stimmen: analyse ausgewählter parameter im kontext von wahrnehmung und klassifikation, vol 6. Reihe Mndliche Kommunikation. Logos Verlag, Berlin

    Google Scholar 

  • Zen G, Lepri B, Ricci E, Lanz O (2010) Space speaks: towards socially and personality aware visual surveillance. In: Proceedings of the 1st ACM international workshop on multimodal pervasive video analysis, p 3742

    Google Scholar 

  • Zuckerman M, Driver RE (1989) What sounds beautiful is good: the vocal attractiveness stereotype. J Nonverbal Behav 13(2):67–82

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Quality and Usability Lab, Telekom Innovation Laboratories, TU Berlin, Berlin, Germany

    Tim Polzehl

Authors
  1. Tim Polzehl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tim Polzehl .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Polzehl, T. (2015). Speech-Based Personality Assessment. In: Personality in Speech. T-Labs Series in Telecommunication Services. Springer, Cham. https://doi.org/10.1007/978-3-319-09516-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09516-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09515-8

  • Online ISBN: 978-3-319-09516-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation