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Abstract:
We describe our submission to the 2024 VoicePrivacy Attacker Challenge. We propose three main categories of methods which improve ASV performance against anonymized speech: improvements to the underlying classifier, alternative distance metrics when computing ASV scores, and kNN-VC normalization. By simultaneously employing one or more of these methods, we were able to achieve a significant reduction in EER against all of the submitted anonymization systems in the VoicePrivacy Challenge.

HLTCOE Submission to the VoicePrivacy Attacker Challenge at ICASSP 2025

November 15, 2013

Abstract
While large-scale discriminative training has triumphed in many NLP problems, its definite success on machine translation has been largely elusive. Most recent efforts along this line are not scalable: they only train on the small dev set with an impoverished set of rather dense features. We instead present a very simple yet theoretically motivated approach by extending my recent framework of violation-fixing perceptron to the latent variable setting, and use forced decoding to compute the target derivations. Our method allows structured learning to scale, for the first time, to a large portion of the training data, which enables a rich set of sparse, lexicalized, and non-local features. Extensive experiments show very significant gains in BLEU (by at least +2.0) over MERT and PRO baselines with the help of over 20M sparse features.

Biography
Liang Huang is currently an Assistant Professor at the City University of New York (CUNY). He graduated in 2008 from Penn and has worked as a Research Scientist at Google and a Research Assistant Professor at USC/ISI. His work is mainly on the theoretical aspects (algorithms and formalisms) of computational linguistics, and related theoretical problems in machine learning. He has received a Best Paper Award at ACL 2008, several best paper nominations (ACL 2007, EMNLP 2008, and ACL 2010), two Google Faculty Research Awards (2010 and 2013), and a University Graduate Teaching Prize at Penn (2005).

Scalable Training for Machine Translation Made Successful for the First Time - Liang Huang (CUNY)

Related paper: https://arxiv.org/abs/2404.04298

Dongwei Jiang: https://www.jiangdongwei.com/

Abstract: Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives, than generating initial responses. We explore the validity of this hypothesis in practice. We first formulate a unified framework that allows us to compare the generative and discriminative capability of any model on any task. In our resulting experimental analysis of several open-source and industrial LLMs, we observe that models are not reliably better at discriminating among previously-generated alternatives than generating initial responses. This finding challenges the notion that LLMs may be able to enhance their performance only through their own judgment.

Self-(In)Correct: LLMs Struggle with Discriminating Self-Generated Responses --- AAAI 2025

Title: CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing (NeurIPS 2024)

Speaker: Yen-ju Lu
https://neillu23.github.io/

Paper:
https://arxiv.org/abs/2412.04425

Abstract:
We introduce Condition-Aware Self-Supervised Learning Representation (CA-SSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SSLR integrates language and speaker embeddings from earlier layers, making the SSL model aware of the current language and speaker context. This approach reduces the reliance on input audio features while preserving the integrity of the base SSLR. CA-SSLR improves the model's capabilities and demonstrates its generality on unseen tasks with minimal task-specific tuning. Our method employs linear modulation to dynamically adjust internal representations, enabling fine-grained adaptability without significantly altering the original model behavior. Experiments show that CA-SSLR reduces the number of trainable parameters, mitigates overfitting, and excels in under-resourced and unseen tasks. Specifically, CA-SSLR achieves a 10% relative reduction in LID errors, a 37% improvement in ASR CER on the ML-SUPERB benchmark, and a 27% decrease in SV EER on VoxCeleb-1, demonstrating its effectiveness.

CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing

Chenhao Tan (UChicago):
https://chenhaot.com/
https://cs.uchicago.edu/people/chenhao-tan/

Abstract
Human preferences have been the main component in guiding pretrained AI. In this talk, I argue that human preferences are often insufficient for complementing human intelligence and demonstrate the key role of human goals with two examples. First, hypothesis generation is critical for scientific discoveries. Instead of removing hallucinations, I will leverage data and labels as a guide to lead hallucination towards effective hypotheses. Second, I will use human perception as a guide for developing case-based explanations to support AI-assisted decision making. In both cases, faithfulness is “compromised” for achieving human goals. Finally, I will briefly discuss the case of identifying bias mechanisms in AI, where faithfulness is crucial. I will conclude with future directions towards complementary AI.

Bio
Tan is an assistant professor of computer science and data science at the University of Chicago, and is also affiliated with the Harris School of Public Policy. He obtained his PhD degree in the Department of Computer Science at Cornell University and bachelor’s degrees in computer science and in economics from Tsinghua University. Prior to joining the University of Chicago, he was an assistant professor at the University of Colorado Boulder and a postdoc at the University of Washington. His research interests include human-centered AI, natural language processing, and computational social science. His work has been covered by many news media outlets, such as the New York Times and the Washington Post. He also won a Sloan research fellowship, an NSF CAREER award, an NSF CRII award, a Google research scholar award, research awards from Amazon, IBM, JP Morgan, and Salesforce, a Facebook fellowship, and a Yahoo! Key Scientific Challenges award.

Beyond Human Preferences: Use Human Goals to Guide AI towards Complementary AI -- Chenhao Tan

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