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Personality in Speech: Assessment and Automatic Classification 2015 ed. [Kõva köide]

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  • Formaat: Hardback, 176 pages, kõrgus x laius: 235x155 mm, kaal: 4144 g, 34 Illustrations, color; 5 Illustrations, black and white; XIV, 176 p. 39 illus., 34 illus. in color., 1 Hardback
  • Sari: T-Labs Series in Telecommunication Services
  • Ilmumisaeg: 11-Sep-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319095153
  • ISBN-13: 9783319095158
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Personality in Speech: Assessment and Automatic Classification 2015 ed.Suurem pilt
  • Formaat: Hardback, 176 pages, kõrgus x laius: 235x155 mm, kaal: 4144 g, 34 Illustrations, color; 5 Illustrations, black and white; XIV, 176 p. 39 illus., 34 illus. in color., 1 Hardback
  • Sari: T-Labs Series in Telecommunication Services
  • Ilmumisaeg: 11-Sep-2014
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319095153
  • ISBN-13: 9783319095158
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319095158.html
Märksõnad:
This work combines interdisciplinary knowledge and experience from research fields of psychology, linguistics, audio-processing, machine learning, and computer science. The work systematically explores a novel research topic devoted to automated modeling of personality expression from speech. For this aim, it introduces a novel personality assessment questionnaire and presents the results of extensive labeling sessions to annotate the speech data with personality assessments. It provides estimates of the Big 5 personality traits, i.e. openness, conscientiousness, extroversion, agreeableness, andneuroticism. Based on a database built on the questionnaire, the book presents models to tell apart different personality types or classes from speech automatically.

Personality Assessment in Psychology.- Speech-based Personality Assessment.- Database and Labeling.- Analysis of Human Personality Perception.- Automatic Personality Estimation.- Discussion.- Conclusion and Outlook.

Arvustused

The focus of this study, written by a researcher at Telekom Innovation Laboratories in Berlin, is speech. Researchers and innovators in human-computer interactions will find interesting and valuable interdisciplinary materials in these experiments. detailed sectional divisions within the chapters, collected in the table of contents, will usually be adequate to rapidly aid the reader in finding the particular desired information. This is cutting-edge research with many potential applications. (Brad Reid, Computing Reviews, November, 2015)

Polzehl reviews research on speech and personality, including descriptive, correlational, and experimental work. He develops a rich corpus of speech and extracts a large set of acoustic features in order to predict the Big Five personality traits . I recommend the book for those interested in the technical details of how to develop an automated speech analysis system. (David S. Kreiner, PsycCRITIQUES, Vol. 60 (33), August, 2015)

1 Personality Assessment in Psychology
1(20)
1.1 Definitions of Personality
1(2)
1.2 Trait Theory of Personality
3(13)
1.2.1 Allport's Trait Organization
3(1)
1.2.2 Eysenck's P-E-N Theory of Super Factors
4(1)
1.2.3 Cattell's 16 Personality Source Traits
5(3)
1.2.4 Development of the "Big 5" Factor Inventories
8(1)
1.2.5 Costa and McCrae and the "NEO-" Inventories
9(3)
1.2.6 The Development of the German NEO-FFI
12(1)
1.2.7 Super Short Forms of Five Factor Inventories
13(1)
1.2.8 Trait Theories Comparison and Criticism
14(2)
1.3 Summary
16(5)
References
17(4)
2 Speech-Based Personality Assessment
21(22)
2.1 Contemporary Terminology in Speech Analysis
24(1)
2.1.1 Prosodic Information and their Temporal Expansion
24(1)
2.1.2 Extralinguistic and Paralinguistic Information
25(1)
2.2 Vocal Cues of Personality Perception
25(10)
2.2.1 Linking Perceptual Speech Properties and Personality
26(2)
2.2.2 Correlation between Perceptual Personality and Acoustic Measurements
28(1)
2.2.3 Work From Speech Synthesis
29(2)
2.2.4 Signal-Based Automated Personality Modeling
31(2)
2.2.5 Other Related Studies
33(1)
2.2.6 Own Prior Work
34(1)
2.3
Chapter Summary
35(8)
References
37(6)
3 Database and Labeling
43(12)
3.1 Text-Dependent Data Recordings
44(1)
3.2 Text-Independent Data Recordings
45(3)
3.3 Multi-Speaker Data Recordings
48(1)
3.4 Annotating the Recordings with Personality Ratings
49(2)
3.5 Summary
51(4)
References
53(2)
4 Analysis of Human Personality Perception
55(26)
4.1 Auditory Impressions and Personality Perception Hypotheses
56(3)
4.2 Distributions and Correlation Analysis of Rating Responses
59(9)
4.3 Factor Analyzing the NEO-FFI Item Responses
68(3)
4.4 Analyses of Variance
71(7)
4.5 Summary
78(3)
References
79(2)
5 Automatic Personality Estimation
81(58)
5.1 Automatic Extraction of Personality Cues from Speech
82(6)
5.1.1 Intensity
82(1)
5.1.2 Pitch
83(2)
5.1.3 Spectrals
85(1)
5.1.4 Loudness
86(1)
5.1.5 MFCC
86(1)
5.1.6 Formants
87(1)
5.1.7 Other Descriptors
88(1)
5.2 Voice Activity Detection and Segmentation
88(1)
5.3 Feature Definition
89(7)
5.3.1 Intensity
90(2)
5.3.2 Pitch
92(2)
5.3.3 Spectrals
94(1)
5.3.4 Loudness
95(1)
5.3.5 MFCC
95(1)
5.3.6 Formants
95(1)
5.3.7 Other Features
95(1)
5.4 Feature Selection
96(3)
5.5 Normalization
99(1)
5.6 Modeling for Personality Classification and Trait Score Prediction using SVM
100(7)
5.6.1 Personality Classification Using SVM
101(2)
5.6.2 Trait Score Prediction Using SVM
103(2)
5.6.3 Non-Linear Mapping and Parameter Tuning
105(2)
5.7 Evaluation
107(3)
5.7.1 Evaluation Metrics
107(2)
5.7.2 Evaluation Method
109(1)
5.8 Results from Text-Dependent Data
110(10)
5.8.1 Results from Automatic Classification
110(4)
5.8.2 Results from Automatic Trait Score Prediction
114(6)
5.9 Results from Text-Independent Data
120(8)
5.9.1 Results from Automatic Classification
120(6)
5.9.2 Results from Automatic Trait Score Prediction
126(2)
5.10 Results from Multi-Speaker Data
128(11)
5.10.1 Results from Automatic Trait Score Prediction
130(7)
References
137(2)
6 Discussion of the Results
139(24)
6.1 Results from Classification of Personality Targets
139(4)
6.2 Prediction of Individual Traits Scores from Speech
143(10)
6.2.1 Prediction of Openness
144(2)
6.2.2 Prediction of Conscientiousness
146(1)
6.2.3 Prediction of Extroversion
147(2)
6.2.4 Prediction of Agreeableness
149(2)
6.2.5 Prediction of Neuroticism
151(2)
6.3 Discussion of Influencing Factors
153(10)
6.3.1 Design of Speech Database
153(6)
6.3.2 Signal-Based Feature Extraction and Model Set-up
159(3)
References
162(1)
7 Conclusion and Outlook
163(10)
7.1 Contributions and Conclusions
163(5)
7.2 Outlook
168(5)
References
172(1)
Appendix A Label Distributions Text-Dependent Recordings 173(2)
Appendix B Label Distributions Text-Independent Recordings 175