-
Notifications
You must be signed in to change notification settings - Fork 0
Literature Review: Lie Detection Using Speech Processing Techniques
In security, law, and psychology; truth verification is crucial process. Conventional techniques use devices such as the lie detector setting screen people for physiological reactions. But these approaches are highly error-prone and invasive by nature, which often receive backlash. However, with the advance in technology, new speech processing techniques are an emerging alternative for lie detection. Techniques that Measure AP, RP, or EM of Speech During Deception These techniques measure acoustic (e.g., pitch, speaking rate), prosodic (e.g., rhythm, emphasis) and emotional (e.g., stress, anxiety) alterations in speech pattern during a lie.
The fusion of machine learning and deep learning algorithms in this field has shown promise in improving detection accuracy. However, challenges such as limited datasets, language and cultural differences, and difficulties in testing under realistic conditions limit their applicability. The purpose of this study is to review existing work on lie detection using language processing techniques, evaluate the strengths and weaknesses of current approaches, and provide guidance for future research.
Acoustic features are critical for detecting changes in speech that may indicate deception. These include fundamental frequency (pitch), formant frequency, intensity, and speech rate.
- Ekman [1] found that fundamental frequency increases under stress, a common indicator of deception. This finding was critical in linking stress-induced acoustic changes to lie detection.
- J. Hirschberg et al. [2] showed that deceptive people often change their speech rate, either speeding up due to nervousness or slowing down due to cognitive load. Machine learning models trained on these features achieved significant improvements in accuracy.
Further research showed that changes in formant frequency and changes in voice intensity may further improve lie detection. These findings highlight the importance of acoustic analysis in detecting speech patterns associated with deception.
Prosodic analysis focuses on the rhythm, intonation, and stress in speech. These features can provide insights into the speaker's emotional and cognitive states, which are often disturbed during deception.
- E. Shriberg et al. [3] observed longer pauses, irregular rhythms, and unnatural intonation patterns in deceptive speech. Such deviations from normal prosody indicate the cognitive and emotional burden associated with lying.
- Prosodic features can also complement acoustic features to provide richer datasets for machine learning models. For example, combining pitch changes with pause duration can significantly improve the accuracy of deception detection systems.
Emotion recognition in speech has been shown to be a key component of lie detection. Emotions such as stress, anxiety, and fear often accompany deceptive behavior and are therefore valuable indicators.
- V. Pérez-Rosas, R. Mihalcea, and A. Narvaez. [4] used emotion recognition algorithms to detect deception with over 80% accuracy. Their work highlighted the effectiveness of analyzing vocal expressions of stress and anxiety.
- Advanced emotion analysis systems can now detect subtle changes in pitch and intensity that are usually imperceptible to human listeners but are highly suggestive of deception.
In general, machine learning algorithms have heavily been used to classify features from speech using automatic analysis in lie detection. Commonly used are the Support Vector Machines, Decision Trees, and Naive Bayes.
- In the work done by C. Fuller, D. Biros, and R. Wilson. [5], an SVM model analyzing speech features was able to realize an accuracy rate of 85%. The critical point taken from this study was that the selection of acoustic and prosodic features for speech deception detection must be properly done for better results. Various Decision Tree-based models have also achieved promising results when finding deception patterns by combining them with feature selection techniques for model training optimization.
Despite all these successes, these traditional models usually perform poorly with more complex data. Due to this limitation, a deep learning approach has started to be adopted, which is capable of processing higher volumes of data more effectively.
Deep learning models allow high-dimensional feature analyses because of their architecture; thus, models like CNN, RNN, and LSTM have started a revolution in deception detection.
- S. I. Levitan, M. An, and J. Hirschberg. [6] used an LSTM model on the speech segments as time series for 90% accuracy. Again, this outlined the strengths of deep learning for finding deception through the dynamic speech features. Different researchers, while extracting the spatial features from spectrograms, which are visual representations of speech signals, employed CNNs. These models showed a lot of promise for spotting acoustic and prosodic patterns connected to deception.
Deep learning techniques also provide transfer learning whereby pre-trained models could be fine-tuned for specific tasks. This has been one of the helpful features of the models, given that one of the major challenges to lie detection research involves the scarcity of labeled datasets.
The development and evaluation of these lie detection systems depend on datasets. Several datasets have been created specifically for this purpose:
- Columbia SRI-Colorado Corpus (CSC): It contains annotated speech data for prosodic and acoustic analysis. This dataset is a benchmark dataset for lie detection models.
- Deceptive Speech Corpus: There is also speech samples that are labeled as truthful or deceptive, so it is a foundational resource for the machine learning experiments.
- Real-life Trial Corpus: It consists of speech data from real court room scenarios, where valuable insights of application of lie detection techniques in the real world are available.
However, these datasets have permitted great leaps forward in the field, but lack sufficient size and diversity to promote model generalization. To further develop our results, future research should involve building larger and more varied dataset such that it covers more languages, cultures, and contexts.[7]
Unfortunately, robust lie detection models are limited by the rarity of available labeled datasets. Traditional datasets are small and not diverse which makes them not suitable for training and testing machine learning algorithms [8].
Language and culture play a major role in speech feature variation, which poses challenges towards developing languages and culture independent models. For instance, intonation and pitch patterns easily vary dramatically across tonal and non tonal languages and are problematic when trying to apply lie detection techniques to a wide array of populations.
In controlled environments, lie detection systems often perform well, but rarely so in real-life. In fact, background noise, overlapping speech and variability of the speakers as well can significantly affect model performance [9].
The challenges identified in the literature highlight the need for innovative approaches to advance the field:
- Multimodal Systems: By combining speech or facial expressions, gestures, or physiological signals, it is possible to improve detection accuracy. These modalities require advanced data fusion techniques and real time processing capabilities to be integrated.
- Real-Time Analysis: For practical applications, we need to be able to develop systems that are able to do real time lie detection.This is because we need to optimize algorithms for speed and scalability, while keeping accuracy.
- Transfer Learning: It can also help alleviate the impact of scarce datasets and shorten training time by leveraging pre-trained models in similar domains, for instance linguistic modelling as in emotion recognition ; or modelling based on high dimensional data spaces like speech synthesis.
In this literature review, a systematic analysis of lie detection through speech processing techniques has been carried out. This study explores the use of speech derived acoustic, prosodic, emotional features with the use of machine learning and deep learning methods with the aim of increasing lie detection accuracy.
However, these advances are not sufficient, as challenges remain, including data paucity, cultural and linguistic variations, and the challenge in deploying such techniques in real world scenarios. To address these limitations, novel approaches must be developed: multimodal systems, transfer learning, and diverse, high quality datasets.
Finally, we conclude that speech processing techniques present a respectable route to lie detection. Nevertheless, more research is needed to realize the full potential of these directionalities. This work lays the groundwork for future research by providing a guide to shortcomings and advances in the field.
[1] P. Ekman, Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage, 4th ed., W.W. Norton, 2009.
[2] J. Hirschberg et al., "Prosody and deception detection," Proceedings of Speech Prosody 2004, 2004, pp. 123-132.
DOI: 10.21437/SpeechProsody.2004-12
[3] E. Shriberg et al., "Acoustic-prosodic indicators of deception in speech: Cross-corpus evaluation," Proceedings of IEEE ICASSP 2012, 2012, pp. 4829-4832.
[4] V. Pérez-Rosas, R. Mihalcea, and A. Narvaez, "Cross-cultural deception detection," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 440-450.
[5] C. Fuller, D. Biros, and R. Wilson, "Decision support for deception detection: A machine learning approach," Decision Support Systems, vol. 46, no. 3, pp. 673-684, 2009.
DOI: 10.1016/j.dss.2008.11.007
[6] S. I. Levitan, M. An, and J. Hirschberg, "Acoustic-prosodic and lexical cues to deception and trust: Deciphering variation across gender, personality, and culture," Proceedings of Interspeech 2018, 2018, pp. 409-413.
[7] A. Vrij, Detecting Lies and Deceit: Pitfalls and Opportunities, 2nd ed., Wiley, 2008.
Link: https://www.wiley.com/en-us/Detecting+Lies+and+Deceit%3A+Pitfalls+and+Opportunities%2C+2nd+Edition-p-9780470516249
[8] T. Baltrušaitis, P. Robinson, and L.-P. Morency, "OpenFace: An open source facial behavior analysis toolkit," Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2016, pp. 1-10.
DOI: 10.1109/WACV.2016.7477553
[9] A. Kumar, S. G. Koolagudi, and K. S. Rao, "Speech emotion recognition using deep learning," International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 116-121.