Investigating social interaction strategies for bootstrapping lexicon development

This paper investigates how different modes of social interactions influence the bootstrapping and evolution of lexicons. This is done by comparing three language game models that differ in the type of social interactions they use. The simulations show that the language games which use either joint attention or corrective feedback as a source of contextual input are better capable of bootstrapping a lexicon than the game without such directed interactions. The simulation of the latter game, however, does show that it is possible to develop a lexicon without using directed input when the lexicon is transmitted from generation to generation

Falsity, Insincerity, and the Freedom of Expression

Three decades ago, the Supreme Court announced that false statements of fact are devoid of constitutional value, without providing either a reasoned explanation for that principle or any supporting citations. This assertion has become one of the most frequently repeated dogmas of First Amendment law and theory, endlessly repeated and never challenged. Disturbingly, this idea has provided the theoretic foundation for a regime in which some speakers can be penalized for even honestly-believed factual errors. Even worse, this dogma is flat wrong. False statements often have value in themselves, and we should protect them even in some situations where we are not concerned with chilling truthful speech. When false statements are spoken sincerely, they are a useful and necessary part of argumentation, which is a powerful means of increasing human knowledge. When confronted with honest errors, proponents of competing beliefs have a natural impulse to contest them; in so doing, they unearth and disseminate facts that deepen the understanding of both speakers and listeners. False speech, therefore, is valuable because it is an essential part of a larger system that works to increase society\u27s knowledge. The benefits of false speech evaporate, however, when we move from honest errors to deliberate lies. Insincere speech tends to corrode, rather than further, argument. It is associated with a number of practices that deprive argument of its knowledge-promoting features. We may sometimes wish to protect insincere speech to avoid chilling truthful speech, but we should always do so cautiously. After providing a summary of the existing law and scholarship concerning false speech, this Article analyzes the harms and benefits of false, insincere, and misleading speech. This question will be approached from the perspective of social veritistic epistemology, which will permit a detailed assessment of the consequences of various types of deceptive speech for the state of societal knowledge. I will conclude by suggesting some ways in which existing First Amendment doctrine could be reformed in order to better account for the constitutional value of false speech. Ultimately, it is insincerity, not falsity, which has no essential part of any exposition of ideas, and is of slight social value as a step to truth. Chaplinsky v. New Hampshire, 315 U.S. 568, 572 (1942). Even a false statement may be deemed to make a valuable contribution to public debate, since it brings about the clearer perception and livelier impression of truth, produced by its collision with error. -- New York Times Co. v. Sullivan (quoting J.S. Mill) (1964)1 [T]here is no constitutional value in false statements of fact. Neither the intentional lie nor the careless error materially advances society\u27s interest in uninhibited, robust and wide-open debate on public issues. -- Gertz v. Robert Welch, Inc. (1974)

QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems

Current adversarial attacks against speaker recognition systems (SRSs) require either white-box access or heavy black-box queries to the target SRS, thus still falling behind practical attacks against proprietary commercial APIs and voice-controlled devices. To fill this gap, we propose QFA2SR, an effective and imperceptible query-free black-box attack, by leveraging the transferability of adversarial voices. To improve transferability, we present three novel methods, tailored loss functions, SRS ensemble, and time-freq corrosion. The first one tailors loss functions to different attack scenarios. The latter two augment surrogate SRSs in two different ways. SRS ensemble combines diverse surrogate SRSs with new strategies, amenable to the unique scoring characteristics of SRSs. Time-freq corrosion augments surrogate SRSs by incorporating well-designed time-/frequency-domain modification functions, which simulate and approximate the decision boundary of the target SRS and distortions introduced during over-the-air attacks. QFA2SR boosts the targeted transferability by 20.9%-70.7% on four popular commercial APIs (Microsoft Azure, iFlytek, Jingdong, and TalentedSoft), significantly outperforming existing attacks in query-free setting, with negligible effect on the imperceptibility. QFA2SR is also highly effective when launched over the air against three wide-spread voice assistants (Google Assistant, Apple Siri, and TMall Genie) with 60%, 46%, and 70% targeted transferability, respectively.Comment: Accepted by the 32nd USENIX Security Symposium (2023 USENIX Security); Full Versio

Security Check-In Station

The major qualifying project is the culmination of lab and courses over four years. The Security Check-In Station is a device which communicates with a central server to give access to guards based on RFID badge verification and voice authentication. The device is designed to have guards check in with the central server showing the patrolled area. By using RFID tags and scanners, and using signal analysis techniques like frequency comparing and signal covariance, the device is able to distinguish guards from imposters

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy