Put your money where your mouth is: Using AI voice analysis to detect whether spoken arguments reflect the speaker's true convictions

Customers’ emotions play a vital role in the service industry. The better frontline personnel understand the customer, the better the service they can provide. As human emotions generate certain (unintentional) bodily reactions, such as increase in heart rate, sweating, dilation, blushing and paling, which are measurable, artificial intelligence (AI) technologies can interpret these signals. Great progress has been made in recent years to automatically detect basic emotions like joy, anger etc. Complex emotions, consisting of multiple interdependent basic emotions, are more difficult to identify. One complex emotion which is of great interest to the service industry is difficult to detect: whether a customer is telling the truth or just a story... This research presents an AI-method for capturing and sensing emotional data. With an accuracy of around 98%, the best trained model was able to detect whether a participant of a debating challenge was arguing for or against her/his conviction, using speech analysis. The data set was collected in an experimental setting with 40 participants. The findings are applicable to a wide range of service processes and specifically useful for all customer interactions that take place via telephone. The algorithm presented can be applied in any situation where it is helpful for the agent to know whether a customer is speaking to her/his conviction. This could, for example, lead to a reduction in doubtful insurance claims, or untruthful statements in job interviews. This would not only reduce operational losses for service companies, but also encourage customers to be more truthful.

[1]  Olga Perepelkina,et al.  "Was It You Who Stole 500 Rubles?" - The Multimodal Deception Detection , 2020, ICMI Companion.

[2]  Geoffrey Finch,et al.  Linguistic terms and concepts , 1999 .

[3]  J. Russell A circumplex model of affect. , 1980 .

[4]  Kathleen C. Fraser,et al.  Linguistic Features Identify Alzheimer's Disease in Narrative Speech. , 2015, Journal of Alzheimer's disease : JAD.

[5]  Leena Mathur,et al.  Introducing Representations of Facial Affect in Automated Multimodal Deception Detection , 2020, ICMI.

[6]  Poonam Bansal,et al.  The State of the Art of Feature Extraction Techniques in Speech Recognition , 2018 .

[7]  Jianfeng Zhao,et al.  Speech emotion recognition using deep 1D & 2D CNN LSTM networks , 2019, Biomed. Signal Process. Control..

[8]  Andrew Mercer,et al.  Why 2016 election polls missed their mark , 2016 .

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Zhenqi Li,et al.  A Review of Emotion Recognition Using Physiological Signals , 2018, Sensors.

[11]  P. Shaver,et al.  Emotion knowledge: further exploration of a prototype approach. , 1987, Journal of personality and social psychology.

[12]  Mohamed Abouelenien,et al.  Multimodal deception detection , 2018, The Handbook of Multimodal-Multisensor Interfaces, Volume 2.

[13]  Tiranee Achalakul,et al.  Emotional healthcare system: Emotion detection by facial expressions using Japanese database , 2014, 2014 6th Computer Science and Electronic Engineering Conference (CEEC).

[14]  David Matsumoto,et al.  Microexpressions Differentiate Truths From Lies About Future Malicious Intent , 2018, Front. Psychol..

[15]  Louis-Philippe Morency,et al.  Computational Analysis of Persuasiveness in Social Multimedia: A Novel Dataset and Multimodal Prediction Approach , 2014, ICMI.

[16]  Beth Logan,et al.  Mel Frequency Cepstral Coefficients for Music Modeling , 2000, ISMIR.

[17]  R. Adolphs Neural systems for recognizing emotion , 2002, Current Opinion in Neurobiology.

[18]  Emily Mower Provost,et al.  Emotion Recognition from Natural Phone Conversations in Individuals with and without Recent Suicidal Ideation , 2019, INTERSPEECH.

[19]  Klaus R. Scherer,et al.  Toward a Working Definition of Emotion , 2012 .

[20]  V. Tiwari MFCC and its applications in speaker recognition , 2010 .

[21]  John W. Merrill,et al.  Automatic Speech Recognition , 2005 .

[22]  S. Lalitha,et al.  Emotion Detection Using MFCC and Cepstrum Features , 2015 .

[23]  C. Carter,et al.  Bluffs, Lies, and Consequences: A Reconceptualization of Bluffing in Buyer–Supplier Negotiations , 2018 .

[24]  A. Malik,et al.  The Influences of Emotion on Learning and Memory , 2017, Front. Psychol..

[25]  M. Zrigui,et al.  Emotion recognition from speech using spectrograms and shallow neural networks , 2020, MoMM.

[26]  Giovanna Sannino,et al.  Voice Disorder Identification by Using Machine Learning Techniques , 2018, IEEE Access.

[27]  Karen Locke A Funny Thing Happened! The Management of Consumer Emotions in Service Encounters , 1996 .

[28]  Hsinchun Chen,et al.  A Deep Learning Architecture for Psychometric Natural Language Processing , 2020, ACM Trans. Inf. Syst..

[29]  Mohammad Soleymani,et al.  Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection , 2016, IEEE Transactions on Affective Computing.

[30]  Mohamed M. Dessouky,et al.  Effective Features Extracting Approach Using MFCC for Automated Diagnosis of Alzheimer's Disease , 2014 .

[31]  Erik Cambria,et al.  A Deep Learning Approach for Multimodal Deception Detection , 2018, CICLing.

[32]  James G. Maxham,et al.  Corridors of Influence in the Dissemination of Customer-Oriented Strategy to Customer Contact Service Employees , 2000 .

[33]  Adel M. Alimi,et al.  ReLiDSS: Novel lie detection system from speech signal , 2016, 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA).

[34]  Tara N. Sainath,et al.  Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[35]  M. Picheny,et al.  Comparison of Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences , 2017 .

[36]  T. Ormerod,et al.  Aviation security by consent using the Controlled Cognitive Engagement (CCE) alternative screening programme , 2020 .

[37]  R. Adorni,et al.  Can You Catch a Liar? How Negative Emotions Affect Brain Responses when Lying or Telling the Truth , 2013, PloS one.

[38]  George Trigeorgis,et al.  Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[39]  Semiye Demircan,et al.  Feature Extraction from Speech Data for Emotion Recognition , 2014 .

[40]  Umut Uludag,et al.  Extraction of Novel Features Based on Histograms of MFCCs Used in Emotion Classification from Generated Original Speech Dataset , 2020 .

[41]  A. Mattila,et al.  The Role of Emotions in Service Encounters , 2002 .

[42]  German Speech Recognition System using DeepSpeech , 2020, NLPIR.

[43]  B. Depaulo,et al.  Accuracy of Deception Judgments , 2006, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[44]  T. Stern,et al.  Lies in the doctor-patient relationship. , 2009, Primary care companion to the Journal of clinical psychiatry.

[45]  Ameya Rajendra Bhamare,et al.  Deep Neural Networks for Lie Detection with Attention on Bio-signals , 2020, 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI).

[46]  Michael K. Brady,et al.  Rise of the Machines? Customer Engagement in Automated Service Interactions , 2021 .

[47]  Sudarsan Vs,et al.  Voice call analytics using natural language processing , 2019 .

[48]  M. A. Novotny,et al.  An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection , 2018, Comput. Networks.

[49]  K. V. Krishna Kishore,et al.  Emotion recognition in speech using MFCC and wavelet features , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[50]  Rada Mihalcea,et al.  DialogueRNN: An Attentive RNN for Emotion Detection in Conversations , 2018, AAAI.

[51]  S. Tokuno,et al.  Usage of emotion recognition in military health care , 2011, 2011 Defense Science Research Conference and Expo (DSR).

[52]  R. Plutchik Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice , 2016 .

[53]  Margaret Lech,et al.  Evaluating deep learning architectures for Speech Emotion Recognition , 2017, Neural Networks.

[54]  N. Amir,et al.  Towards an automatic classification of emotions in speech , 1998, ICSLP.

[55]  Ashish Semwal,et al.  Detection of emotion in analysis of speech using linear predictive coding techniques (L.P.C) , 2017, 2017 International Conference on Inventive Systems and Control (ICISC).

[56]  Roland T. Rust,et al.  Artificial Intelligence in Service , 2018 .

[57]  P. Ekman Emotions Revealed : Understanding Faces and Feelings , 2003 .

[58]  K. Scherer Expression of emotion in voice and music. , 1995, Journal of voice : official journal of the Voice Foundation.

[59]  Tuure Tuunanen,et al.  Advancing service design research with design science research , 2019 .

[60]  Roland T. Rust,et al.  A strategic framework for artificial intelligence in marketing , 2020, Journal of the Academy of Marketing Science.

[61]  Bruce G. Buchanan,et al.  A (Very) Brief History of Artificial Intelligence , 2005, AI Mag..

[62]  Samir Chatterjee,et al.  A Design Science Research Methodology for Information Systems Research , 2008 .

[63]  Greg Linden,et al.  Two Decades of Recommender Systems at Amazon.com , 2017, IEEE Internet Computing.

[64]  Alok N. Choudhary,et al.  Voice of the Customers: Mining Online Customer Reviews for Product Feature-based Ranking , 2010, WOSN.

[65]  Björn W. Schuller,et al.  Hidden Markov model-based speech emotion recognition , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[66]  Bruno Verschuere,et al.  Psychopathy and Physiological Detection of Concealed Information: A review , 2006 .

[67]  Julia Hirschberg,et al.  LieCatcher: Game Framework for Collecting Human Judgments of Deceptive Speech , 2020, ICMI.

[68]  Thaweesak Yingthawornsuk,et al.  Speech Recognition using MFCC , 2012 .

[69]  Lynn Sudbury-Riley,et al.  The Trajectory Touchpoint Technique: A Deep Dive Methodology for Service Innovation , 2020 .

[70]  Louis-Philippe Morency,et al.  Deep multimodal fusion for persuasiveness prediction , 2016, ICMI.

[71]  Roland T. Rust,et al.  The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI) , 2019, California Management Review.

[72]  Jon Elster,et al.  Strong Feelings: Emotion, Addiction, and Human Behavior , 1999 .

[73]  E. B. Newman,et al.  A Scale for the Measurement of the Psychological Magnitude Pitch , 1937 .

[74]  Yu Zhang,et al.  Very deep convolutional networks for end-to-end speech recognition , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[75]  Namrata Dave,et al.  Feature Extraction Methods LPC, PLP and MFCC In Speech Recognition , 2013 .

[76]  Jianhua Zhang,et al.  Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review , 2020, Inf. Fusion.

[77]  Sylvie Fainzang,et al.  Lying, secrecy and power within the doctor-patient relationship , 2002, Anthropology & medicine.