pwc-archive/papers-with-abstracts huggingface dataset:
Comparison of Oracles
Authors: David Lagziel, Ehud Lehrer, Tao Wang
Date: 2025-05-21 00:00:00

We analyze incomplete-information games where an oracle publicly shares information with players. One oracle dominates another if, in every game, it can match the set of equilibrium outcomes induced by the latter. Distinct characterizations are provided for deterministic and stochastic signaling functions, based on simultaneous posterior matching, partition refinements, and common knowledge components. This study extends the work of Blackwell (1951) to games, and expands the study of Aumann (1976) on common knowledge by developing a theory of information loops.
Trial and Return Option Strategy in Omnichannel Retailing
Authors: Yasuyuki Kusuda
Date: 2025-05-21 00:00:00

This study examines the dynamics of customer behavior with trial and return options in omnichannel retailing, where retailers face challenges in integrating physical and online stores. Recently, major retailers have begun offering customers the option of trying eligible items for a set period and returning unwanted products free of charge. However, existing research has not fully explored the temporal dynamics of customer return behaviors. This study investigates how temporal dynamics affect customer return behaviors and decision-making during trial periods. Using a theoretical game structure framework, this study explores customer decision patterns regarding store visits, product trials, and returns, while examining the strategic role of store clerks in encouraging product trials. The findings suggest that retailers can maximize profit through trial and return options when product fit probability is low, emphasizing the importance of maintaining complementary physical and online channels. We also found that store clerks play a critical role in encouraging customers to try misfit products. The results further reveal that return cost coverage policies may not significantly impact customer behavior or retailer profit.
Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation
Authors: Ofir Yaish, Yehuda Mishaly, Eliya Nachmani
Date: 2025-05-22 00:00:00
Tasks: Denoising, Mamba, Speech Denoising, Speech Enhancement

We introduce a new paradigm for active sound modification: Active Speech Enhancement (ASE). While Active Noise Cancellation (ANC) algorithms focus on suppressing external interference, ASE goes further by actively shaping the speech signal -- both attenuating unwanted noise components and amplifying speech-relevant frequencies -- to improve intelligibility and perceptual quality. To enable this, we propose a novel Transformer-Mamba-based architecture, along with a task-specific loss function designed to jointly optimize interference suppression and signal enrichment. Our method outperforms existing baselines across multiple speech processing tasks -- including denoising, dereverberation, and declipping -- demonstrating the effectiveness of active, targeted modulation in challenging acoustic environments.
SEED: Speaker Embedding Enhancement Diffusion Model
Authors: Kihyun Nam, Jungwoo Heo, Jee-weon Jung, Gangin Park, Chaeyoung Jung, Ha-Jin Yu, Joon Son Chung
Date: 2025-05-22 00:00:00
Tasks: model, Speaker Recognition

A primary challenge when deploying speaker recognition systems in real-world applications is performance degradation caused by environmental mismatch. We propose a diffusion-based method that takes speaker embeddings extracted from a pre-trained speaker recognition model and generates refined embeddings. For training, our approach progressively adds Gaussian noise to both clean and noisy speaker embeddings extracted from clean and noisy speech, respectively, via forward process of a diffusion model, and then reconstructs them to clean embeddings in the reverse process. While inferencing, all embeddings are regenerated via diffusion process. Our method needs neither speaker label nor any modification to the existing speaker recognition pipeline. Experiments on evaluation sets simulating environment mismatch scenarios show that our method can improve recognition accuracy by up to 19.6% over baseline models while retaining performance on conventional scenarios. We publish our code here https://github.com/kaistmm/seed-pytorch
Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting
Authors: Youngmoon Jung, Yong-Hyeok Lee, Myunghun Jung, Jaeyoung Roh, Chang Woo Han, Hoon-Young Cho
Date: 2025-05-22 00:00:00
Tasks: Keyword Spotting, Metric Learning

For text enrollment-based open-vocabulary keyword spotting (KWS), acoustic and text embeddings are typically compared at either the phoneme or utterance level. To facilitate this, we optimize acoustic and text encoders using deep metric learning (DML), enabling direct comparison of multi-modal embeddings in a shared embedding space. However, the inherent heterogeneity between audio and text modalities presents a significant challenge. To address this, we propose Modality Adversarial Learning (MAL), which reduces the domain gap in heterogeneous modality representations. Specifically, we train a modality classifier adversarially to encourage both encoders to generate modality-invariant embeddings. Additionally, we apply DML to achieve phoneme-level alignment between audio and text, and conduct extensive comparisons across various DML objectives. Experiments on the Wall Street Journal (WSJ) and LibriPhrase datasets demonstrate the effectiveness of the proposed approach.
Performance of Objective Speech Quality Metrics on Languages Beyond Validation Data: A Study of Turkish and Korean
Authors: Javier Perez, Dimme de Groot, Jorge Martinez
Date: 2025-05-22 00:00:00

Objective speech quality measures are widely used to assess the performance of video conferencing platforms and telecommunication systems. They predict human-rated speech quality and are crucial for assessing the systems quality of experience. Despite the widespread use, the quality measures are developed on a limited set of languages. This can be problematic since the performance on unseen languages is consequently not guaranteed or even studied. Here we raise awareness to this issue by investigating the performance of two objective speech quality measures (PESQ and ViSQOL) on Turkish and Korean. Using English as baseline, we show that Turkish samples have significantly higher ViSQOL scores and that for Turkish male speakers the correlation between PESQ and ViSQOL is highest. These results highlight the need to explore biases across metrics and to develop a labeled speech quality dataset with a variety of languages.
HPP-Voice: A Large-Scale Evaluation of Speech Embeddings for Multi-Phenotypic Classification
Authors: David Krongauz, Hido Pinto, Sarah Kohn, Yanir Marmor, Eran Segal
Date: 2025-05-22 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Identification

Human speech contains paralinguistic cues that reflect a speaker's physiological and neurological state, potentially enabling non-invasive detection of various medical phenotypes. We introduce the Human Phenotype Project Voice corpus (HPP-Voice): a dataset of 7,188 recordings in which Hebrew-speaking adults count for 30 seconds, with each speaker linked to up to 15 potentially voice-related phenotypes spanning respiratory, sleep, mental health, metabolic, immune, and neurological conditions. We present a systematic comparison of 14 modern speech embedding models, where modern speech embeddings from these 30-second counting tasks outperform MFCCs and demographics for downstream health condition classifications. We found that embedding learned from a speaker identification model can predict objectively measured moderate to severe sleep apnea in males with an AUC of 0.64 $\pm$ 0.03, while MFCC and demographic features led to AUCs of 0.56 $\pm$ 0.02 and 0.57 $\pm$ 0.02, respectively. Additionally, our results reveal gender-specific patterns in model effectiveness across different medical domains. For males, speaker identification and diarization models consistently outperformed speech foundation models for respiratory conditions (e.g., asthma: 0.61 $\pm$ 0.03 vs. 0.56 $\pm$ 0.02) and sleep-related conditions (insomnia: 0.65 $\pm$ 0.04 vs. 0.59 $\pm$ 0.05). For females, speaker diarization models performed best for smoking status (0.61 $\pm$ 0.02 vs 0.55 $\pm$ 0.02), while Hebrew-specific models performed best (0.59 $\pm$ 0.02 vs. 0.58 $\pm$ 0.02) in classifying anxiety compared to speech foundation models. Our findings provide evidence that a simple counting task can support large-scale, multi-phenotypic voice screening and highlight which embedding families generalize best to specific conditions, insights that can guide future vocal biomarker research and clinical deployment.
On the reliability of feature attribution methods for speech classification
Authors: Gaofei Shen, Hosein Mohebbi, Arianna Bisazza, Afra Alishahi, Grzegorz Chrupała
Date: 2025-05-22 00:00:00

As the capabilities of large-scale pre-trained models evolve, understanding the determinants of their outputs becomes more important. Feature attribution aims to reveal which parts of the input elements contribute the most to model outputs. In speech processing, the unique characteristics of the input signal make the application of feature attribution methods challenging. We study how factors such as input type and aggregation and perturbation timespan impact the reliability of standard feature attribution methods, and how these factors interact with characteristics of each classification task. We find that standard approaches to feature attribution are generally unreliable when applied to the speech domain, with the exception of word-aligned perturbation methods when applied to word-based classification tasks.
Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge
Authors: Ming Cheng, Fei Su, Cancan Li, Juan Liu, Ming Li
Date: 2025-05-22 00:00:00
Tasks: speaker-diarization, Speaker Diarization

This paper describes the speaker diarization system developed for the Multimodal Information-Based Speech Processing (MISP) 2025 Challenge. First, we utilize the Sequence-to-Sequence Neural Diarization (S2SND) framework to generate initial predictions using single-channel audio. Then, we extend the original S2SND framework to create a new version, Multi-Channel Sequence-to-Sequence Neural Diarization (MC-S2SND), which refines the initial results using multi-channel audio. The final system achieves a diarization error rate (DER) of 8.09% on the evaluation set of the competition database, ranking first place in the speaker diarization task of the MISP 2025 Challenge.
Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detection
Authors: Chenxu Guo, Jiachen Lian, Xuanru Zhou, Jinming Zhang, Shuhe Li, Zongli Ye, Hwi Joo Park, Anaisha Das, Zoe Ezzes, Jet Vonk, Brittany Morin, Rian Bogley, Lisa Wauters, Zachary Miller, Maria Gorno-Tempini, Gopala Anumanchipalli
Date: 2025-05-22 00:00:00
Tasks: Decoder

Automatic detection of speech dysfluency aids speech-language pathologists in efficient transcription of disordered speech, enhancing diagnostics and treatment planning. Traditional methods, often limited to classification, provide insufficient clinical insight, and text-independent models misclassify dysfluency, especially in context-dependent cases. This work introduces Dysfluent-WFST, a zero-shot decoder that simultaneously transcribes phonemes and detects dysfluency. Unlike previous models, Dysfluent-WFST operates with upstream encoders like WavLM and requires no additional training. It achieves state-of-the-art performance in both phonetic error rate and dysfluency detection on simulated and real speech data. Our approach is lightweight, interpretable, and effective, demonstrating that explicit modeling of pronunciation behavior in decoding, rather than complex architectures, is key to improving dysfluency processing systems.
Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning
Authors: Liang-Yeh Shen, Shi-Xin Fang, Yi-Cheng Lin, Huang-Cheng Chou, Hung-Yi Lee
Date: 2025-05-22 00:00:00
Tasks: Emotion Recognition, Meta-Learning, Speech Emotion Recognition

This paper introduces Meta-PerSER, a novel meta-learning framework that personalizes Speech Emotion Recognition (SER) by adapting to each listener's unique way of interpreting emotion. Conventional SER systems rely on aggregated annotations, which often overlook individual subtleties and lead to inconsistent predictions. In contrast, Meta-PerSER leverages a Model-Agnostic Meta-Learning (MAML) approach enhanced with Combined-Set Meta-Training, Derivative Annealing, and per-layer per-step learning rates, enabling rapid adaptation with only a few labeled examples. By integrating robust representations from pre-trained self-supervised models, our framework first captures general emotional cues and then fine-tunes itself to personal annotation styles. Experiments on the IEMOCAP corpus demonstrate that Meta-PerSER significantly outperforms baseline methods in both seen and unseen data scenarios, highlighting its promise for personalized emotion recognition.
Large Language Models based ASR Error Correction for Child Conversations
Authors: Anfeng Xu, Tiantian Feng, So Hyun Kim, Somer Bishop, Catherine Lord, Shrikanth Narayanan
Date: 2025-05-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic Speech Recognition (ASR) has recently shown remarkable progress, but accurately transcribing children's speech remains a significant challenge. Recent developments in Large Language Models (LLMs) have shown promise in improving ASR transcriptions. However, their applications in child speech including conversational scenarios are underexplored. In this study, we explore the use of LLMs in correcting ASR errors for conversational child speech. We demonstrate the promises and challenges of LLMs through experiments on two children's conversational speech datasets with both zero-shot and fine-tuned ASR outputs. We find that while LLMs are helpful in correcting zero-shot ASR outputs and fine-tuned CTC-based ASR outputs, it remains challenging for LLMs to improve ASR performance when incorporating contextual information or when using fine-tuned autoregressive ASR (e.g., Whisper) outputs.
AudioMorphix: Training-free audio editing with diffusion probabilistic models
Authors: Jinhua Liang, Yuanzhe Chen, Yi Yuan, Dongya Jia, Xiaobin Zhuang, Zhuo Chen, Yuping Wang, Yuxuan Wang
Date: 2025-05-21 00:00:00

Editing sound with precision is a crucial yet underexplored challenge in audio content creation. While existing works can manipulate sounds by text instructions or audio exemplar pairs, they often struggled to modify audio content precisely while preserving fidelity to the original recording. In this work, we introduce a novel editing approach that enables localized modifications to specific time-frequency regions while keeping the remaining of the audio intact by operating on spectrograms directly. To achieve this, we propose AudioMorphix, a training-free audio editor that manipulates a target region on the spectrogram by referring to another recording. Inspired by morphing theory, we conceptualize audio mixing as a process where different sounds blend seamlessly through morphing and can be decomposed back into individual components via demorphing. Our AudioMorphix optimizes the noised latent conditioned on raw input and reference audio while rectifying the guided diffusion process through a series of energy functions. Additionally, we enhance self-attention layers with a cache mechanism to preserve detailed characteristics from the original recordings. To advance audio editing research, we devise a new evaluation benchmark, which includes a curated dataset with a variety of editing instructions. Extensive experiments demonstrate that AudioMorphix yields promising performance on various audio editing tasks, including addition, removal, time shifting and stretching, and pitch shifting, achieving high fidelity and precision. Demo and code are available at this url.
Multimodal Biomarkers for Schizophrenia: Towards Individual Symptom Severity Estimation
Authors: Gowtham Premananth, Philip Resnik, Sonia Bansal, Deanna L. Kelly, Carol Espy-Wilson
Date: 2025-05-21 00:00:00
Tasks: Diagnostic

Studies on schizophrenia assessments using deep learning typically treat it as a classification task to detect the presence or absence of the disorder, oversimplifying the condition and reducing its clinical applicability. This traditional approach overlooks the complexity of schizophrenia, limiting its practical value in healthcare settings. This study shifts the focus to individual symptom severity estimation using a multimodal approach that integrates speech, video, and text inputs. We develop unimodal models for each modality and a multimodal framework to improve accuracy and robustness. By capturing a more detailed symptom profile, this approach can help in enhancing diagnostic precision and support personalized treatment, offering a scalable and objective tool for mental health assessment.
Analyzing the Impact of Accent on English Speech: Acoustic and Articulatory Perspectives
Authors: Gowtham Premananth, Vinith Kugathasan, Carol Espy-Wilson
Date: 2025-05-21 00:00:00

Advancements in AI-driven speech-based applications have transformed diverse industries ranging from healthcare to customer service. However, the increasing prevalence of non-native accented speech in global interactions poses significant challenges for speech-processing systems, which are often trained on datasets dominated by native speech. This study investigates accented English speech through articulatory and acoustic analysis, identifying simpler coordination patterns and higher average pitch than native speech. Using eigenspectra and Vocal Tract Variable-based coordination features, we establish an efficient method for quantifying accent strength without relying on resource-intensive phonetic transcriptions. Our findings provide a new avenue for research on the impacts of accents on speech intelligibility and offer insights for developing inclusive, robust speech processing systems that accommodate diverse linguistic communities.
Deep mineralogical segmentation of thin section images based on QEMSCAN maps
Authors: Jean Pablo Vieira de Mello, Matheus Augusto Alves Cuglieri, Leandro P. de Figueiredo, Fernando Bordignon, Marcelo Ramalho Albuquerque, Rodrigo Surmas, Bruno Cavalcanti de Paula
Date: 2025-05-22 00:00:00
Tasks: Image Registration, Semantic Segmentation

Interpreting the mineralogical aspects of rock thin sections is an important task for oil and gas reservoirs evaluation. However, human analysis tend to be subjective and laborious. Technologies like QEMSCAN(R) are designed to automate the mineralogical mapping process, but also suffer from limitations like high monetary costs and time-consuming analysis. This work proposes a Convolutional Neural Network model for automatic mineralogical segmentation of thin section images of carbonate rocks. The model is able to mimic the QEMSCAN mapping itself in a low-cost, generalized and efficient manner. For this, the U-Net semantic segmentation architecture is trained on plane and cross polarized thin section images using the corresponding QEMSCAN maps as target, which is an approach not widely explored. The model was instructed to differentiate occurrences of Calcite, Dolomite, Mg-Clay Minerals, Quartz, Pores and the remaining mineral phases as an unique class named "Others", while it was validated on rock facies both seen and unseen during training, in order to address its generalization capability. Since the images and maps are provided in different resolutions, image registration was applied to align then spatially. The study reveals that the quality of the segmentation is very much dependent on these resolution differences and on the variety of learnable rock textures. However, it shows promising results, especially with regard to the proper delineation of minerals boundaries on solid textures and precise estimation of the minerals distributions, describing a nearly linear relationship between expected and predicted distributions, with coefficient of determination (R^2) superior to 0.97 for seen facies and 0.88 for unseen.
SEDD-PCC: A Single Encoder-Dual Decoder Framework For End-To-End Learned Point Cloud Compression
Authors: Kai Hsiang Hsieh, Monyneath Yim, Jui Chiu Chiang
Date: 2025-05-22 00:00:00
Tasks: Attribute, Decoder, Knowledge Distillation, Representation Learning

To encode point clouds containing both geometry and attributes, most learning-based compression schemes treat geometry and attribute coding separately, employing distinct encoders and decoders. This not only increases computational complexity but also fails to fully exploit shared features between geometry and attributes. To address this limitation, we propose SEDD-PCC, an end-to-end learning-based framework for lossy point cloud compression that jointly compresses geometry and attributes. SEDD-PCC employs a single encoder to extract shared geometric and attribute features into a unified latent space, followed by dual specialized decoders that sequentially reconstruct geometry and attributes. Additionally, we incorporate knowledge distillation to enhance feature representation learning from a teacher model, further improving coding efficiency. With its simple yet effective design, SEDD-PCC provides an efficient and practical solution for point cloud compression. Comparative evaluations against both rule-based and learning-based methods demonstrate its competitive performance, highlighting SEDD-PCC as a promising AI-driven compression approach.
One-Step Diffusion-Based Image Compression with Semantic Distillation
Authors: Naifu Xue, Zhaoyang Jia, Jiahao Li, Bin Li, Yuan Zhang, Yan Lu
Date: 2025-05-22 00:00:00
Tasks: Image Compression

While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasing latency. In this work, we revisit the design of a diffusion-based codec and argue that multi-step sampling is not necessary for generative compression. Based on this insight, we propose OneDC, a One-step Diffusion-based generative image Codec -- that integrates a latent compression module with a one-step diffusion generator. Recognizing the critical role of semantic guidance in one-step diffusion, we propose using the hyperprior as a semantic signal, overcoming the limitations of text prompts in representing complex visual content. To further enhance the semantic capability of the hyperprior, we introduce a semantic distillation mechanism that transfers knowledge from a pretrained generative tokenizer to the hyperprior codec. Additionally, we adopt a hybrid pixel- and latent-domain optimization to jointly enhance both reconstruction fidelity and perceptual realism. Extensive experiments demonstrate that OneDC achieves SOTA perceptual quality even with one-step generation, offering over 40% bitrate reduction and 20x faster decoding compared to prior multi-step diffusion-based codecs. Code will be released later.