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1. Age- and Gender-Related Differences in Speech Alignment Toward Humans and Voice-AI

   https://doi.org/10.3389/fcomm.2020.600361  

   Zellou, Georgia; Cohn, Michelle; Ferenc Segedin, Bruno (January 2021, Frontiers in Communication)

   null (Ed.)

   Speech alignment is where talkers subconsciously adopt the speech and language patterns of their interlocutor. Nowadays, people of all ages are speaking with voice-activated, artificially-intelligent (voice-AI) digital assistants through phones or smart speakers. This study examines participants’ age (older adults, 53–81 years old vs. younger adults, 18–39 years old) and gender (female and male) on degree of speech alignment during shadowing of (female and male) human and voice-AI (Apple’s Siri) productions. Degree of alignment was assessed holistically via a perceptual ratings AXB task by a separate group of listeners. Results reveal that older and younger adults display distinct patterns of alignment based on humanness and gender of the human model talkers: older adults displayed greater alignment toward the female human and device voices, while younger adults aligned to a greater extent toward the male human voice. Additionally, there were other gender-mediated differences observed, all of which interacted with model talker category (voice-AI vs. human) or shadower age category (OA vs. YA). Taken together, these results suggest a complex interplay of social dynamics in alignment, which can inform models of speech production both in human-human and human-device interaction. 

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2. Emergent AI-assisted discourse: a case study of a second language writer authoring with ChatGPT

   https://doi.org/10.1515/jccall-2024-0011  

   Jacob, Sharin R; Tate, Tamara; Warschauer, Mark (October 2023, Journal of China Computer-Assisted Language Learning)

   Abstract The rapid proliferation of ChatGPT has incited debates regarding its impact on human writing. Amid concerns about declining writing standards, this study investigates the role of ChatGPT in facilitating writing, especially among language learners. Using a case study approach, this study examines the experiences of Kailing, a doctoral student, who integrates ChatGPT throughout their writing process. The study employs activity theory as a lens for understanding writing with generative AI tools and data analyzed includes semi-structured interviews, writing samples, and GPT logs. Results indicate that Kailing effectively collaborates with ChatGPT across various writing stages while preserving her distinct authorial voice and agency. This underscores the potential of AI tools such as ChatGPT to enhance writing for language learners without overshadowing individual authenticity. This case study offers a critical exploration of how ChatGPT is utilized in the writing process and the preservation of a student’s authentic voice when engaging with the tool. 

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3. Strategic Adversarial Attacks in AI-assisted Decision Making to Reduce Human Trust and Reliance.

   Lu, Zhuoran; Li, Zhuoyan; Chiang, Chun-Wei; Yin, Ming (August 2023, Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23))

   With the increased integration of AI technologies in human decision making processes, adversarial attacks on AI models become a greater concern than ever before as they may significantly hurt humans’ trust in AI models and decrease the effectiveness of human-AI collaboration. While many adversarial attack methods have been proposed to decrease the performance of an AI model, limited attention has been paid on understanding how these attacks will impact the human decision makers interacting with the model, and accordingly, how to strategically deploy adversarial attacks to maximize the reduction of human trust and reliance. In this paper, through a human-subject experiment, we first show that in AI-assisted decision making, the timing of the attacks largely influences how much humans decrease their trust in and reliance on AI—the decrease is particularly salient when attacks occur on decision making tasks that humans are highly confident themselves. Based on these insights, we next propose an algorithmic framework to infer the human decision maker’s hidden trust in the AI model and dynamically decide when the attacker should launch an attack to the model. Our evaluations show that following the proposed approach, attackers deploy more efficient attacks and achieve higher utility than adopting other baseline strategies. 

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4. Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

   https://doi.org/10.1007/s10462-023-10539-8  

   Khan, Awais; Malik, Khalid Mahmood; Ryan, James; Saravanan, Mikul (January 2023, Artificial Intelligence Review)

   With the advent of automated speaker verifcation (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofng attacks to fool those same systems. Various counter measures have been proposed to detect these spoofing attacks, but current oferings in this arena fall short of a unifed and generalized approach applicable in real-world scenarios. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classifed, and qualitative and quantitative comparisons of state-of-the-art (SOTA) counter measures should be performed to assess the efectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofng detection using hand-crafted features, deep learning, and end-to-end spoofng countermeasure solutions to detect logical access attacks, such as speech synthesis and voice conversion, and physical access attacks, i.e., replay attacks. Additionally, we review integrated and unifed solutions to voice spoofng evaluation and speaker verifcation, and adversarial and anti-forensic attacks on both voice counter measures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofng counter measures are presented, the performance of these counter measures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifers. For reproducibility of the results, the code of the testbed can be found at our GitHub Repository (https://github.com/smileslab/Comparative-Analysis-Voice-Spoofing). 

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5. CommanderGabble: A Universal Attack Against ASR Systems Leveraging Fast Speech

   https://doi.org/10.1145/3485832.3485892  

   Zhang, Zhaohe; Yang, Edwin; Fang, Song (December 2021, Annual Computer Security Applications Conference (ACSAC))

   Automatic Speech Recognition (ASR) systems are widely used in various online transcription services and personal digital assistants. Emerging lines of research have demonstrated that ASR systems are vulnerable to hidden voice commands, i.e., audio that can be recognized by ASRs but not by humans. Such attacks, however, often either highly depend on white-box knowledge of a specific machine learning model or require special hardware to construct the adversarial audio. This paper proposes a new model-agnostic and easily-constructed attack, called CommanderGabble, which uses fast speech to camouflage voice commands. Both humans and ASR systems often misinterpret fast speech, and such misinterpretation can be exploited to launch hidden voice command attacks. Specifically, by carefully manipulating the phonetic structure of a target voice command, ASRs can be caused to derive a hidden meaning from the manipulated, high-speed version. We implement the discovered attacks both over-the-wire and over-the-air, and conduct a suite of experiments to demonstrate their efficacy against 7 practical ASR systems. Our experimental results show that the over-the-wire attacks can disguise as many as 96 out of 100 tested voice commands into adversarial ones, and that the over-the-air attacks are consistently successful for all 18 chosen commands in multiple real-world scenarios. 

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