Silent Talker Lie Detector

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The Silent Talker Psychological Profiler is a camera system which observes and analyses non-verbal behaviour in the form of micro-gestures while a subject is being interviewed, for the claimed purpose of credibility assessment. It is grounded in the psychological theory that non-verbal behaviour is modified by a number of influences when a person is being deceptive. These include arousal (in particular stress), cognitive load, duping delight,[1] and behaviour control.[2]


Silent Talker was invented between 2000 and 2002 by a team at Manchester Metropolitan University, Zuhair Bandar, James O'Shea, David McLean and Janet Rothwell. Following its invention, the Silent Talker Adaptive Psychological Profiling architecture and its specific instantiation as a lie detector, were patented internationally.[3] In the interim, the inventors have been involved in raising investment funding and the code has been ported to various programming languages and speeded up from near real-time to real-time response. Current research includes adapting the technology to the measurement of comprehension amongst participants giving informed consent to take part in clinical trials [4] Silent Talker Limited was incorporated on 9 April 2015 to commercialize this technology worldwide.

Testing procedure[edit]

The subject of the interview is observed by one or more cameras (e.g. head-and-shoulders, full body view, thermal imaging camera), which input the video stream to a conventional computer. As the interview takes place, Silent Talker's model of truthful vs. deceptive behaviour is used to classify the answers to the questions as truthful or deceptive in real-time. This can be as a classification at the end of the answer to a question or as a continuous monitoring stream during the interview. No calibration is required to tune the system to individuals and no training of the interviewer is required to interpret the Silent Talker classifications.


The fundamental phenomenon behind Silent Talker is non-verbal behaviour. Non-verbal behaviour is a well-established field of academic study with its origins in the work of Charles Darwin.[5] Modern analysis of non-verbal behaviour at a fine-grained temporal level has its origins in the work of Efron.[6] Investigations of training humans to detect truth and deceit conducted by Vrij et al. provide evidence to support the effectiveness of non-verbal behaviour as a predictive feature.[7]

Artificial neural networks have been described as having a "remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques",[8] they have been established as a subfield of artificial intelligence for over 60 years and are the subject of dedicated, high impact journals such as Neural Networks. Consequently, they provide a scientifically credible basis for Silent Talker's classifiers.[improper synthesis?]

The distinctive features of Silent Talker are:

  • It uses banks of artificial neural network classifiers to identify features.
  • It uses further banks of artificial neural network classifiers to detect microgestures.
  • The microgestures are coded into channels over a time period.
  • The relationships between events in the channels over the time period are analysed by artificial neural networks to make the classification.
  • The artificial neural networks were trained using video data collected from experiments.
  • Thus the classifier artificial neural networks discovered which features were important and the relationships between them that discriminate between deceptive and truthful non-verbal behaviour.

Silent Talker has been published in peer-reviewed journals for both the psychology[9] and artificial intelligence[10] communities.


As other lie detectors detect changes in stress, the most common approach is to disrupt this either by practicing calming techniques during lying or artificially raising stress during truth-telling. Because Silent Talker is based on a multi-factor model including cognitive load, duping delight and behaviour control, its inventors claim that it is robust to countermeasures. In fact it is believed[according to whom?] that because a large number of channels are used, attempts at behaviour control will generate more incongruities between channels which can be detected. Further experimental trials are required to investigate this hypothesis.


Challenges with this technological approach include:

  • it relies on only one channel, the face; excludes body language, voice, verbal content, verbal style and psychophysiology
  • it concludes from data without hypothesizing; operators play no role in the decision making
  • it relies on an intrusive camera within a few metres of the face
  • it doesn't analyse what the person is saying so expression can be correlated with the account.


  1. ^ Ekman. P. Lying And Nonverbal Behavior – Theoretical Issues And New Findings. Journal of Nonverbal Behavior, 1988, 12, 163–175. "Archived copy" (PDF). Archived from the original (PDF) on 2011-11-12. Retrieved 2011-10-13.CS1 maint: Archived copy as title (link)
  2. ^ Greene, John O. (1985). "Planning and Control of Behavoir During Deception". Human Communication Research. 11 (3): 335–364. doi:10.1111/j.1468-2958.1985.tb00051.x.
  3. ^ Bandar, J., McLean, D., O'Shea, J. and Rothwell, J. ANALYSIS OF THE BEHAVIOUR OF A SUBJECT WO02087443,
  4. ^ Crockett, Keeley A.; O'Shea, James D.; Buckingham, Fiona J.; Bandar, Zuhair A.; MacQueen, Kathleen.M.; Chen, Mario; Simpson, Kelly. FATHOMing out interdisciplinary research transfer, IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES), 2013
  5. ^ Darwin, Charles (1872), The expression of the emotions in man and animals, London: John Murray.
  6. ^ Efron, D. (1941), Gesture and environment. New York: King's Crown.
  7. ^ Vrij, Aldert, Evans, Hayley, Akehurst, Lucy and Mann, Samantha (2004) Rapid judgements in assessing verbal and nonverbal cues: their potential for deception researchers and lie detection. Applied Cognitive Psychology, 18 (3). pp. 283–296. ISSN 0888-4080 doi:10.1002/acp964
  8. ^ Stergiou, Christos and Siganos, Dimitrios, NEURAL NETWORKS, Imperial College Website, retrieved 13/10/2011, use neural networks
  9. ^ Rothwell, J. Bandar, Z. O'Shea, J. McLean, D. Silent talker: a new computer-based system for the analysis of facial cues to deception, Journal of Applied Cognitive Psychology, Volume 20, Issue 6, pp. 757–777, September 2006. doi:10.1002/acp.1204. Authors' self-archived version:, Final article format:
  10. ^ Rothwell, J. Bandar, Z. O'Shea, J. McLean, D. Charting the behavioural state of a person using a Backpropagation Neural Network. Journal of Neural Computing and Applications. doi:10.1007/s00521-006-0055-9. 2006. Authors' self-archived version:, Final article format:

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