pwc-archive/papers-with-abstracts huggingface dataset:
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning
Authors: Abdulhady Abas Abdullah, Sarkhel H. Taher Karim, Sara Azad Ahmed, Kanar R. Tariq, Tarik A. Rashid
Date: 2025-04-23 00:00:00
Tasks: Self-Supervised Learning, speaker-diarization, Speaker Diarization, Transfer Learning

Speaker diarization is a fundamental task in speech processing that involves dividing an audio stream by speaker. Although state-of-the-art models have advanced performance in high-resource languages, low-resource languages such as Kurdish pose unique challenges due to limited annotated data, multiple dialects and frequent code-switching. In this study, we address these issues by training the Wav2Vec 2.0 self-supervised learning model on a dedicated Kurdish corpus. By leveraging transfer learning, we adapted multilingual representations learned from other languages to capture the phonetic and acoustic characteristics of Kurdish speech. Relative to a baseline method, our approach reduced the diarization error rate by seven point two percent and improved cluster purity by thirteen percent. These findings demonstrate that enhancements to existing models can significantly improve diarization performance for under-resourced languages. Our work has practical implications for developing transcription services for Kurdish-language media and for speaker segmentation in multilingual call centers, teleconferencing and video-conferencing systems. The results establish a foundation for building effective diarization systems in other understudied languages, contributing to greater equity in speech technology.
LLM-based Automated Grading with Human-in-the-Loop
Authors: Hang Li, Yucheng Chu, Kaiqi Yang, Yasemin Copur-Gencturk, Jiliang Tang
Date: 2025-04-07 00:00:00
Tasks: automatic short answer grading

The rise of artificial intelligence (AI) technologies, particularly large language models (LLMs), has brought significant advancements to the field of education. Among various applications, automatic short answer grading (ASAG), which focuses on evaluating open-ended textual responses, has seen remarkable progress with the introduction of LLMs. These models not only enhance grading performance compared to traditional ASAG approaches but also move beyond simple comparisons with predefined "golden" answers, enabling more sophisticated grading scenarios, such as rubric-based evaluation. However, existing LLM-powered methods still face challenges in achieving human-level grading performance in rubric-based assessments due to their reliance on fully automated approaches. In this work, we explore the potential of LLMs in ASAG tasks by leveraging their interactive capabilities through a human-in-the-loop (HITL) approach. Our proposed framework, GradeHITL, utilizes the generative properties of LLMs to pose questions to human experts, incorporating their insights to refine grading rubrics dynamically. This adaptive process significantly improves grading accuracy, outperforming existing methods and bringing ASAG closer to human-level evaluation.
Context Selection and Rewriting for Video-based Educational Question Generation
Authors: Mengxia Yu, Bang Nguyen, Olivia Zino, Meng Jiang
Date: 2025-04-28 00:00:00
Tasks: Active Learning, Question Generation, Question-Generation

Educational question generation (EQG) is a crucial component of intelligent educational systems, significantly aiding self-assessment, active learning, and personalized education. While EQG systems have emerged, existing datasets typically rely on predefined, carefully edited texts, failing to represent real-world classroom content, including lecture speech with a set of complementary slides. To bridge this gap, we collect a dataset of educational questions based on lectures from real-world classrooms. On this realistic dataset, we find that current methods for EQG struggle with accurately generating questions from educational videos, particularly in aligning with specific timestamps and target answers. Common challenges include selecting informative contexts from extensive transcripts and ensuring generated questions meaningfully incorporate the target answer. To address the challenges, we introduce a novel framework utilizing large language models for dynamically selecting and rewriting contexts based on target timestamps and answers. First, our framework selects contexts from both lecture transcripts and video keyframes based on answer relevance and temporal proximity. Then, we integrate the contexts selected from both modalities and rewrite them into answer-containing knowledge statements, to enhance the logical connection between the contexts and the desired answer. This approach significantly improves the quality and relevance of the generated questions. Our dataset and code are released in https://github.com/mengxiayu/COSER.
SoCov: Semi-Orthogonal Parametric Pooling of Covariance Matrix for Speaker Recognition
Authors: Rongjin Li, Weibin Zhang, Dongpeng Chen, Jintao Kang, Xiaofen Xing
Date: 2025-04-23 00:00:00
Tasks: Speaker Recognition

In conventional deep speaker embedding frameworks, the pooling layer aggregates all frame-level features over time and computes their mean and standard deviation statistics as inputs to subsequent segment-level layers. Such statistics pooling strategy produces fixed-length representations from variable-length speech segments. However, this method treats different frame-level features equally and discards covariance information. In this paper, we propose the Semi-orthogonal parameter pooling of Covariance matrix (SoCov) method. The SoCov pooling computes the covariance matrix from the self-attentive frame-level features and compresses it into a vector using the semi-orthogonal parametric vectorization, which is then concatenated with the weighted standard deviation vector to form inputs to the segment-level layers. Deep embedding based on SoCov is called ``sc-vector''. The proposed sc-vector is compared to several different baselines on the SRE21 development and evaluation sets. The sc-vector system significantly outperforms the conventional x-vector system, with a relative reduction in EER of 15.5% on SRE21Eval. When using self-attentive deep feature, SoCov helps to reduce EER on SRE21Eval by about 30.9% relatively to the conventional ``mean + standard deviation'' statistics.
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning
Authors: Cheng Wen, Tingwei Guo, Shuaijiang Zhao, Wei Zou, Xiangang Li
Date: 2025-04-22 00:00:00
Tasks: Multiple-choice, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent work shows that reinforcement learning(RL) can markedly sharpen the reasoning ability of large language models (LLMs) by prompting them to "think before answering." Yet whether and how these gains transfer to audio-language reasoning remains largely unexplored. We extend the Group-Relative Policy Optimization (GRPO) framework from DeepSeek-R1 to a Large Audio-Language Model (LALM), and construct a 32k sample multiple-choice corpus. Using a two-stage regimen supervised fine-tuning on structured and unstructured chains-of-thought, followed by curriculum-guided GRPO, we systematically compare implicit vs. explicit, and structured vs. free form reasoning under identical architectures. Our structured audio reasoning model, SARI (Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning), achieves a 16.35% improvement in average accuracy over the base model Qwen2-Audio-7B-Instruct. Furthermore, the variant built upon Qwen2.5-Omni reaches state-of-the-art performance of 67.08% on the MMAU test-mini benchmark. Ablation experiments show that on the base model we use: (i) SFT warm-up is important for stable RL training, (ii) structured chains yield more robust generalization than unstructured ones, and (iii) easy-to-hard curricula accelerate convergence and improve final performance. These findings demonstrate that explicit, structured reasoning and curriculum learning substantially enhances audio-language understanding.
Unified Multi-Task Learning & Model Fusion for Efficient Language Model Guardrailing
Authors: James O' Neill, Santhosh Subramanian, Eric Lin, Vaikkunth Mugunthan
Date: 2025-04-27 00:00:00
Tasks: Language Modeling, Language Modelling, model, Multi-Task Learning, Synthetic Data Generation

The trend towards large language models (LLMs) for guardrailing against undesired behaviors is increasing and has shown promise for censoring user inputs. However, increased latency, memory consumption, hosting expenses and non-structured outputs can make their use prohibitive. In this work, we show that task-specific data generation can lead to fine-tuned classifiers that significantly outperform current state of the art (SoTA) while being orders of magnitude smaller. Secondly, we show that using a single model, \texttt{MultiTaskGuard}, that is pretrained on a large synthetically generated dataset with unique task instructions further improves generalization. Thirdly, our most performant models, \texttt{UniGuard}, are found using our proposed search-based model merging approach that finds an optimal set of parameters to combine single-policy models and multi-policy guardrail models. % On 7 public datasets and 4 guardrail benchmarks we created, our efficient guardrail classifiers improve over the best performing SoTA publicly available LLMs and 3$^{\text{rd}}$ party guardrail APIs in detecting unsafe and safe behaviors by an average F1 score improvement of \textbf{29.92} points over Aegis-LlamaGuard and \textbf{21.62} over \texttt{gpt-4o}, respectively. Lastly, our guardrail synthetic data generation process that uses custom task-specific guardrail poli
DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation
Authors: Zhibo Man, Yuanmeng Chen, Yujie Zhang, Jinan Xu
Date: 2025-04-29 00:00:00
Tasks: Machine Translation, Translation

Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation. However, their performance in multi-domain translation (MDT) is less satisfactory, the meanings of words can vary across different domains, highlighting the significant ambiguity inherent in MDT. Therefore, evaluating the disambiguation ability of LLMs in MDT, remains an open problem. To this end, we present an evaluation and analysis of LLMs on disambiguation in multi-domain translation (DMDTEval), our systematic evaluation framework consisting of three critical aspects: (1) we construct a translation test set with multi-domain ambiguous word annotation, (2) we curate a diverse set of disambiguation prompt strategies, and (3) we design precise disambiguation metrics, and study the efficacy of various prompt strategies on multiple state-of-the-art LLMs. We conduct comprehensive experiments across 4 language pairs and 13 domains, our extensive experiments reveal a number of crucial findings that we believe will pave the way and also facilitate further research in the critical area of improving the disambiguation of LLMs.
Deep, data-driven modeling of room acoustics: literature review and research perspectives
Authors: Toon van Waterschoot
Date: 2025-04-22 00:00:00

Our everyday auditory experience is shaped by the acoustics of the indoor environments in which we live. Room acoustics modeling is aimed at establishing mathematical representations of acoustic wave propagation in such environments. These representations are relevant to a variety of problems ranging from echo-aided auditory indoor navigation to restoring speech understanding in cocktail party scenarios. Many disciplines in science and engineering have recently witnessed a paradigm shift powered by deep learning (DL), and room acoustics research is no exception. The majority of deep, data-driven room acoustics models are inspired by DL-based speech and image processing, and hence lack the intrinsic space-time structure of acoustic wave propagation. More recently, DL-based models for room acoustics that include either geometric or wave-based information have delivered promising results, primarily for the problem of sound field reconstruction. In this review paper, we will provide an extensive and structured literature review on deep, data-driven modeling in room acoustics. Moreover, we position these models in a framework that allows for a conceptual comparison with traditional physical and data-driven models. Finally, we identify strengths and shortcomings of deep, data-driven room acoustics models and outline the main challenges for further research.
TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation
Authors: Ling You, Wenxuan Huang, Xinni Xie, Xiangyi Wei, Bangyan Li, Shaohui Lin, Yang Li, Changbo Wang
Date: 2025-04-24 00:00:00
Tasks: Caption Generation, Dense Video Captioning, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, Temporal Localization, Temporal Sequences, Video Captioning, Video Understanding

Soccer is a globally popular sporting event, typically characterized by long matches and distinctive highlight moments. Recent advances in Multimodal Large Language Models (MLLMs) offer promising capabilities in temporal grounding and video understanding, soccer commentary generation often requires precise temporal localization and semantically rich descriptions over long-form video. However, existing soccer MLLMs often rely on the temporal a priori for caption generation, so they cannot process the soccer video end-to-end. While some traditional approaches follow a two-step paradigm that is complex and fails to capture the global context to achieve suboptimal performance. To solve the above issues, we present TimeSoccer, the first end-to-end soccer MLLM for Single-anchor Dense Video Captioning (SDVC) in full-match soccer videos. TimeSoccer jointly predicts timestamps and generates captions in a single pass, enabling global context modeling across 45-minute matches. To support long video understanding of soccer matches, we introduce MoFA-Select, a training-free, motion-aware frame compression module that adaptively selects representative frames via a coarse-to-fine strategy, and incorporates complementary training paradigms to strengthen the model's ability to handle long temporal sequences. Extensive experiments demonstrate that our TimeSoccer achieves State-of-The-Art (SoTA) performance on the SDVC task in an end-to-end form, generating high-quality commentary with accurate temporal alignment and strong semantic relevance.
Using Phonemes in cascaded S2S translation pipeline
Authors: Rene Pilz, Johannes Schneider
Date: 2025-04-22 00:00:00
Tasks: Simultaneous Speech-to-Speech Translation, Speech-to-Speech Translation, Translation

This paper explores the idea of using phonemes as a textual representation within a conventional multilingual simultaneous speech-to-speech translation pipeline, as opposed to the traditional reliance on text-based language representations. To investigate this, we trained an open-source sequence-to-sequence model on the WMT17 dataset in two formats: one using standard textual representation and the other employing phonemic representation. The performance of both approaches was assessed using the BLEU metric. Our findings shows that the phonemic approach provides comparable quality but offers several advantages, including lower resource requirements or better suitability for low-resource languages.
TinyML for Speech Recognition
Authors: Andrew Barovic, Armin Moin
Date: 2025-04-22 00:00:00
Tasks: speech-recognition, Speech Recognition

We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples. In this paper, we first create a new dataset with over one hour of audio data that enables our research and will be useful to future studies in this field. Second, we utilize the technologies provided by Edge Impulse to enhance our model's performance and achieve a high Accuracy of up to 97% on our dataset. For the validation, we implement our prototype using the Arduino Nano 33 BLE Sense microcontroller board. This microcontroller board is specifically designed for IoT and AI applications, making it an ideal choice for our target use case scenarios. While most existing research focuses on a limited set of keywords, our model can process 23 different keywords, enabling complex commands.
Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows
Authors: Haohe Liu, Thomas Deacon, Wenwu Wang, Matt Paradis, Mark D. Plumbley
Date: 2025-04-22 00:00:00
Tasks: valid

Locating the right sound effect efficiently is an important yet challenging topic for audio production. Most current sound-searching systems rely on pre-annotated audio labels created by humans, which can be time-consuming to produce and prone to inaccuracies, limiting the efficiency of audio production. Following the recent advancement of contrastive language-audio pre-training (CLAP) models, we explore an alternative CLAP-based sound-searching system (CLAP-UI) that does not rely on human annotations. To evaluate the effectiveness of CLAP-UI, we conducted comparative experiments with a widely used sound effect searching platform, the BBC Sound Effect Library. Our study evaluates user performance, cognitive load, and satisfaction through ecologically valid tasks based on professional sound-searching workflows. Our result shows that CLAP-UI demonstrated significantly enhanced productivity and reduced frustration while maintaining comparable cognitive demands. We also qualitatively analyzed the participants' feedback, which offered valuable perspectives on the design of future AI-assisted sound search systems.
What Causes Knowledge Loss in Multilingual Language Models?
Authors: Maria Khelli, Samuel Cahyawijaya, Ayu Purwarianti, Genta Indra Winata
Date: 2025-04-29 00:00:00
Tasks: Cross-Lingual Transfer

Cross-lingual transfer in natural language processing (NLP) models enhances multilingual performance by leveraging shared linguistic knowledge. However, traditional methods that process all data simultaneously often fail to mimic real-world scenarios, leading to challenges like catastrophic forgetting, where fine-tuning on new tasks degrades performance on previously learned ones. Our study explores this issue in multilingual contexts, focusing on linguistic differences affecting representational learning rather than just model parameters. We experiment with 52 languages using LoRA adapters of varying ranks to evaluate non-shared, partially shared, and fully shared parameters. Our aim is to see if parameter sharing through adapters can mitigate forgetting while preserving prior knowledge. We find that languages using non-Latin scripts are more susceptible to catastrophic forgetting, whereas those written in Latin script facilitate more effective cross-lingual transfer.
Local Prompt Optimization
Authors: Yash Jain, Vishal Chowdhary
Date: 2025-04-29 00:00:00
Tasks: GSM8K, Math, Prompt Engineering

In recent years, the use of prompts to guide the output of Large Language Models have increased dramatically. However, even the best of experts struggle to choose the correct words to stitch up a prompt for the desired task. To solve this, LLM driven prompt optimization emerged as an important problem. Existing prompt optimization methods optimize a prompt globally, where in all the prompt tokens have to be optimized over a large vocabulary while solving a complex task. The large optimization space (tokens) leads to insufficient guidance for a better prompt. In this work, we introduce Local Prompt Optimization (LPO) that integrates with any general automatic prompt engineering method. We identify the optimization tokens in a prompt and nudge the LLM to focus only on those tokens in its optimization step. We observe remarkable performance improvements on Math Reasoning (GSM8k and MultiArith) and BIG-bench Hard benchmarks across various automatic prompt engineering methods. Further, we show that LPO converges to the optimal prompt faster than global methods.
REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations
Authors: Peter Sushko, Ayana Bharadwaj, Zhi Yang Lim, Vasily Ilin, Ben Caffee, Dongping Chen, Mohammadreza Salehi, Cheng-Yu Hsieh, Ranjay Krishna
Date: 2025-02-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: DeepFake Detection, Face Swapping

Existing image editing models struggle to meet real-world demands. Despite excelling in academic benchmarks, they have yet to be widely adopted for real user needs. Datasets that power these models use artificial edits, lacking the scale and ecological validity necessary to address the true diversity of user requests. We introduce REALEDIT, a large-scale image editing dataset with authentic user requests and human-made edits sourced from Reddit. REALEDIT includes a test set of 9300 examples to evaluate models on real user requests. Our results show that existing models fall short on these tasks, highlighting the need for realistic training data. To address this, we introduce 48K training examples and train our REALEDIT model, achieving substantial gains - outperforming competitors by up to 165 Elo points in human judgment and 92 percent relative improvement on the automated VIEScore metric. We deploy our model on Reddit, testing it on new requests, and receive positive feedback. Beyond image editing, we explore REALEDIT's potential in detecting edited images by partnering with a deepfake detection non-profit. Finetuning their model on REALEDIT data improves its F1-score by 14 percentage points, underscoring the dataset's value for broad applications.
SimulS2S-LLM: Unlocking Simultaneous Inference of Speech LLMs for Speech-to-Speech Translation
Authors: Keqi Deng, Wenxi Chen, Xie Chen, Philip C. Woodland
Date: 2025-04-22 00:00:00
Tasks: Simultaneous Speech-to-Speech Translation, Speech-to-Speech Translation, Translation

Simultaneous speech translation (SST) outputs translations in parallel with streaming speech input, balancing translation quality and latency. While large language models (LLMs) have been extended to handle the speech modality, streaming remains challenging as speech is prepended as a prompt for the entire generation process. To unlock LLM streaming capability, this paper proposes SimulS2S-LLM, which trains speech LLMs offline and employs a test-time policy to guide simultaneous inference. SimulS2S-LLM alleviates the mismatch between training and inference by extracting boundary-aware speech prompts that allows it to be better matched with text input data. SimulS2S-LLM achieves simultaneous speech-to-speech translation (Simul-S2ST) by predicting discrete output speech tokens and then synthesising output speech using a pre-trained vocoder. An incremental beam search is designed to expand the search space of speech token prediction without increasing latency. Experiments on the CVSS speech data show that SimulS2S-LLM offers a better translation quality-latency trade-off than existing methods that use the same training data, such as improving ASR-BLEU scores by 3 points at similar latency.
Enhancing Systematic Reviews with Large Language Models: Using GPT-4 and Kimi
Authors: Dandan Chen Kaptur, Yue Huang, Xuejun Ryan Ji, Yanhui Guo, Bradley Kaptur
Date: 2025-04-28 00:00:00

This research delved into GPT-4 and Kimi, two Large Language Models (LLMs), for systematic reviews. We evaluated their performance by comparing LLM-generated codes with human-generated codes from a peer-reviewed systematic review on assessment. Our findings suggested that the performance of LLMs fluctuates by data volume and question complexity for systematic reviews.