pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of LLMs in Speech is Often Flawed: Test Set Contamination in Large Language Models for Speech Recognition
Authors: Yuan Tseng, Titouan Parcollet, Rogier Van Dalen, Shucong Zhang, Sourav Bhattacharya
Date: 2025-05-28 00:00:00
Tasks: speech-recognition, Speech Recognition

Recent work suggests that large language models (LLMs) can improve performance of speech tasks compared to existing systems. To support their claims, results on LibriSpeech and Common Voice are often quoted. However, this work finds that a substantial amount of the LibriSpeech and Common Voice evaluation sets appear in public LLM pretraining corpora. This calls into question the reliability of findings drawn from these two datasets. To measure contamination impact, LLMs trained with/without contamination are compared. A contaminated LLM is more likely to generate test sentences it has seen during training. Then, speech recognisers based on LLMs are compared. They show only subtle error rate differences if the LLM is contaminated, but assign significantly higher probabilities to transcriptions seen during LLM training. Results show that LLM outputs can be biased by tiny amounts of data contamination, highlighting the importance of evaluating LLM-based speech systems with held-out data.
ARiSE: Auto-Regressive Multi-Channel Speech Enhancement
Authors: Pengjie Shen, Xueliang Zhang, Zhong-Qiu Wang
Date: 2025-05-28 00:00:00
Tasks: Speech Enhancement

We propose ARiSE, an auto-regressive algorithm for multi-channel speech enhancement. ARiSE improves existing deep neural network (DNN) based frame-online multi-channel speech enhancement models by introducing auto-regressive connections, where the estimated target speech at previous frames is leveraged as extra input features to help the DNN estimate the target speech at the current frame. The extra input features can be derived from (a) the estimated target speech in previous frames; and (b) a beamformed mixture with the beamformer computed based on the previous estimated target speech. On the other hand, naively training the DNN in an auto-regressive manner is very slow. To deal with this, we propose a parallel training mechanism to speed up the training. Evaluation results in noisy-reverberant conditions show the effectiveness and potential of the proposed algorithms.
FlowSE: Efficient and High-Quality Speech Enhancement via Flow Matching
Authors: Ziqian Wang, Zikai Liu, Xinfa Zhu, Yike Zhu, Mingshuai Liu, Jun Chen, Longshuai Xiao, Chao Weng, Lei Xie
Date: 2025-05-26 00:00:00
Tasks: Quantization, Speech Enhancement

Generative models have excelled in audio tasks using approaches such as language models, diffusion, and flow matching. However, existing generative approaches for speech enhancement (SE) face notable challenges: language model-based methods suffer from quantization loss, leading to compromised speaker similarity and intelligibility, while diffusion models require complex training and high inference latency. To address these challenges, we propose FlowSE, a flow-matching-based model for SE. Flow matching learns a continuous transformation between noisy and clean speech distributions in a single pass, significantly reducing inference latency while maintaining high-quality reconstruction. Specifically, FlowSE trains on noisy mel spectrograms and optional character sequences, optimizing a conditional flow matching loss with ground-truth mel spectrograms as supervision. It implicitly learns speech's temporal-spectral structure and text-speech alignment. During inference, FlowSE can operate with or without textual information, achieving impressive results in both scenarios, with further improvements when transcripts are available. Extensive experiments demonstrate that FlowSE significantly outperforms state-of-the-art generative methods, establishing a new paradigm for generative-based SE and demonstrating the potential of flow matching to advance the field. Our code, pre-trained checkpoints, and audio samples are available.
Leveraging Cascaded Binary Classification and Multimodal Fusion for Dementia Detection through Spontaneous Speech
Authors: Yin-Long Liu, Yuanchao Li, Rui Feng, Liu He, Jia-Xin Chen, Yi-Ming Wang, Yu-Ang Chen, Yan-Han Peng, Jia-Hong Yuan, Zhen-Hua Ling
Date: 2025-05-26 00:00:00
Tasks: Binary Classification, Classification, Ensemble Learning, Multi-class Classification, regression

This paper presents our submission to the PROCESS Challenge 2025, focusing on spontaneous speech analysis for early dementia detection. For the three-class classification task (Healthy Control, Mild Cognitive Impairment, and Dementia), we propose a cascaded binary classification framework that fine-tunes pre-trained language models and incorporates pause encoding to better capture disfluencies. This design streamlines multi-class classification and addresses class imbalance by restructuring the decision process. For the Mini-Mental State Examination score regression task, we develop an enhanced multimodal fusion system that combines diverse acoustic and linguistic features. Separate regression models are trained on individual feature sets, with ensemble learning applied through score averaging. Experimental results on the test set outperform the baselines provided by the organizers in both tasks, demonstrating the robustness and effectiveness of our approach.
FT-Boosted SV: Towards Noise Robust Speaker Verification for English Speaking Classroom Environments
Authors: Saba Tabatabaee, Jing Liu, Carol Espy-Wilson
Date: 2025-05-26 00:00:00
Tasks: Speaker Verification

Creating Speaker Verification (SV) systems for classroom settings that are robust to classroom noises such as babble noise is crucial for the development of AI tools that assist educational environments. In this work, we study the efficacy of finetuning with augmented children datasets to adapt the x-vector and ECAPA-TDNN to classroom environments. We demonstrate that finetuning with augmented children's datasets is powerful in that regard and reduces the Equal Error Rate (EER) of x-vector and ECAPA-TDNN models for both classroom datasets and children speech datasets. Notably, this method reduces EER of the ECAPA-TDNN model on average by half (a 5 % improvement) for classrooms in the MPT dataset compared to the ECAPA-TDNN baseline model. The x-vector model shows an 8 % average improvement for classrooms in the NCTE dataset compared to its baseline.
DuRep: Dual-Mode Speech Representation Learning via ASR-Aware Distillation
Authors: Prabash Reddy Male, Swayambhu Nath Ray, Harish Arsikere, Akshat Jaiswal, Prakhar Swarup, Prantik Sen, Debmalya Chakrabarty, K V Vijay Girish, Nikhil Bhave, Frederick Weber, Sambuddha Bhattacharya, Sri Garimella
Date: 2025-05-26 00:00:00
Tasks: Representation Learning, Speech Representation Learning

Recent advancements in speech encoders have drawn attention due to their integration with Large Language Models for various speech tasks. While most research has focused on either causal or full-context speech encoders, there's limited exploration to effectively handle both streaming and non-streaming applications, while achieving state-of-the-art performance. We introduce DuRep, a Dual-mode Speech Representation learning setup, which enables a single speech encoder to function efficiently in both offline and online modes without additional parameters or mode-specific adjustments, across downstream tasks. DuRep-200M, our 200M parameter dual-mode encoder, achieves 12% and 11.6% improvements in streaming and non-streaming modes, over baseline encoders on Multilingual ASR. Scaling this approach to 2B parameters, DuRep-2B sets new performance benchmarks across ASR and non-ASR tasks. Our analysis reveals interesting trade-offs between acoustic and semantic information across encoder layers.
Navigating PESQ: Up-to-Date Versions and Open Implementations
Authors: Matteo Torcoli, Mhd Modar Halimeh, Emanuël A. P. Habets
Date: 2025-05-26 00:00:00

Perceptual Evaluation of Speech Quality (PESQ) is an objective quality measure that remains widely used despite its withdrawal by the International Telecommunication Union (ITU). PESQ has evolved over two decades, with multiple versions and publicly available implementations emerging during this time. The numerous versions and their updates can be overwhelming, especially for new PESQ users. This work provides practical guidance on the different versions and implementations of PESQ. We show that differences can be significant, especially between PESQ versions. We stress the importance of specifying the exact version and implementation that is used to compute PESQ, and possibly to detail how multi-channel signals are handled. These practices would facilitate the interpretation of results and allow comparisons of PESQ scores between different studies. We also provide a repository that implements the latest corrections to PESQ, i.e., Corrigendum 2, which is not implemented by any other openly available distribution: https://github.com/audiolabs/PESQ.
A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions
Authors: Zheng Wang, Xiaobin Rong, Yu Sun, Tianchi Sun, Zhibin Lin, Jing Lu
Date: 2025-05-26 00:00:00
Tasks: Speech Enhancement

Although deep learning based multi-channel speech enhancement has achieved significant advancements, its practical deployment is often limited by constrained computational resources, particularly in low signal-to-noise ratio (SNR) conditions. In this paper, we propose a lightweight hybrid dual-channel speech enhancement system that combines independent vector analysis (IVA) with a modified version of the dual-channel grouped temporal convolutional recurrent network (GTCRN). IVA functions as a coarse estimator, providing auxiliary information for both speech and noise, while the modified GTCRN further refines the speech quality. We investigate several modifications to ensure the comprehensive utilization of both original and auxiliary information. Experimental results demonstrate the effectiveness of the proposed system, achieving enhanced speech with minimal parameters and low computational complexity.
Beyond Manual Transcripts: The Potential of Automated Speech Recognition Errors in Improving Alzheimer's Disease Detection
Authors: Yin-Long Liu, Rui Feng, Jia-Xin Chen, Yi-Ming Wang, Jia-Hong Yuan, Zhen-Hua Ling
Date: 2025-05-26 00:00:00
Tasks: Alzheimer's Disease Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Recent breakthroughs in Automatic Speech Recognition (ASR) have enabled fully automated Alzheimer's Disease (AD) detection using ASR transcripts. Nonetheless, the impact of ASR errors on AD detection remains poorly understood. This paper fills the gap. We conduct a comprehensive study on AD detection using transcripts from various ASR models and their synthesized speech on the ADReSS dataset. Experimental results reveal that certain ASR transcripts (ASR-synthesized speech) outperform manual transcripts (manual-synthesized speech) in detection accuracy, suggesting that ASR errors may provide valuable cues for improving AD detection. Additionally, we propose a cross-attention-based interpretability model that not only identifies these cues but also achieves superior or comparable performance to the baseline. Furthermore, we utilize this model to unveil AD-related patterns within pre-trained embeddings. Our study offers novel insights into the potential of ASR models for AD detection.
Stack Less, Repeat More: A Block Reusing Approach for Progressive Speech Enhancement
Authors: Jangyeon Kim, Ui-Hyeop Shin, Jaehyun Ko, Hyung-Min Park
Date: 2025-05-26 00:00:00
Tasks: Decoder, Speech Enhancement

This paper presents an efficient speech enhancement (SE) approach that reuses a processing block repeatedly instead of conventional stacking. Rather than increasing the number of blocks for learning deep latent representations, repeating a single block leads to progressive refinement while reducing parameter redundancy. We also minimize domain transformation by keeping an encoder and decoder shallow and reusing a single sequence modeling block. Experimental results show that the number of processing stages is more critical to performance than the number of blocks with different weights. Also, we observed that the proposed method gradually refines a noisy input within a single block. Furthermore, with the block reuse method, we demonstrate that deepening the encoder and decoder can be redundant for learning deep complex representation. Therefore, the experimental results confirm that the proposed block reusing enables progressive learning and provides an efficient alternative for SE.
Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction
Authors: Xinmeng Luan, Gary Scavone
Date: 2025-05-25 00:00:00
Tasks: Friction

This study investigates the use of an unsupervised, physics-informed deep learning framework to model a one-degree-of-freedom mass-spring system subjected to a nonlinear friction bow force and governed by a set of ordinary differential equations. Specifically, it examines the application of Physics-Informed Neural Networks (PINNs) and Physics-Informed Deep Operator Networks (PI-DeepONets). Our findings demonstrate that PINNs successfully address the problem across different bow force scenarios, while PI-DeepONets perform well under low bow forces but encounter difficulties at higher forces. Additionally, we analyze the Hessian eigenvalue density and visualize the loss landscape. Overall, the presence of large Hessian eigenvalues and sharp minima indicates highly ill-conditioned optimization. These results underscore the promise of physics-informed deep learning for nonlinear modelling in musical acoustics, while also revealing the limitations of relying solely on physics-based approaches to capture complex nonlinearities. We demonstrate that PI-DeepONets, with their ability to generalize across varying parameters, are well-suited for sound synthesis. Furthermore, we demonstrate that the limitations of PI-DeepONets under higher forces can be mitigated by integrating observation data within a hybrid supervised-unsupervised framework. This suggests that a hybrid supervised-unsupervised DeepONets framework could be a promising direction for future practical applications.
Context-Driven Dynamic Pruning for Large Speech Foundation Models
Authors: Masao Someki, Shikhar Bharadwaj, Atharva Anand Joshi, Chyi-Jiunn Lin, Jinchuan Tian, Jee-weon Jung, Markus Müller, Nathan Susanj, Jing Liu, Shinji Watanabe
Date: 2025-05-24 00:00:00

Speech foundation models achieve strong generalization across languages and acoustic conditions, but require significant computational resources for inference. In the context of speech foundation models, pruning techniques have been studied that dynamically optimize model structures based on the target audio leveraging external context. In this work, we extend this line of research and propose context-driven dynamic pruning, a technique that optimizes the model computation depending on the context between different input frames and additional context during inference. We employ the Open Whisper-style Speech Model (OWSM) and incorporate speaker embeddings, acoustic event embeddings, and language information as additional context. By incorporating the speaker embedding, our method achieves a reduction of 56.7 GFLOPs while improving BLEU scores by a relative 25.7% compared to the fully fine-tuned OWSM model.
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation
Authors: Xiangyu Zhang, Fuming Fang, Peng Gao, Bin Qin, Beena Ahmed, Julien Epps
Date: 2025-05-24 00:00:00
Tasks: Quantization, Representation Learning

The tokenization of speech with neural speech codec models is a crucial aspect of AI systems designed for speech understanding and generation. While text-based systems naturally benefit from token boundaries between discrete symbols, tokenizing continuous speech signals is more complex due to the unpredictable timing of important acoustic variations. Most current neural speech codecs typically address this by using uniform processing at fixed time intervals, which overlooks the varying information density inherent in speech. In this paper, we introduce a distinctive feature-based approach that dynamically allocates tokens based on the perceptual significance of speech content. By learning to identify and prioritize distinctive regions in speech signals, our approach achieves a significantly more efficient speech representation compared with conventional frame-based methods. This work marks the first successful extension of traditional signal processing-based distinctive features into deep learning frameworks. Through rigorous experimentation, we demonstrate the effectiveness of our approach and provide theoretical insights into how aligning segment boundaries with natural acoustic transitions improves codebook utilization. Additionally, we enhance tokenization stability by developing a Group-wise Scalar Quantization approach for variable-length segments. Our distinctive feature-based approach offers a promising alternative to conventional frame-based processing and advances interpretable representation learning in the modern deep learning speech processing framework.
CHSER: A Dataset and Case Study on Generative Speech Error Correction for Child ASR
Authors: Natarajan Balaji Shankar, Zilai Wang, Kaiyuan Zhang, Mohan Shi, Abeer Alwan
Date: 2025-05-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic Speech Recognition (ASR) systems struggle with child speech due to its distinct acoustic and linguistic variability and limited availability of child speech datasets, leading to high transcription error rates. While ASR error correction (AEC) methods have improved adult speech transcription, their effectiveness on child speech remains largely unexplored. To address this, we introduce CHSER, a Generative Speech Error Correction (GenSEC) dataset for child speech, comprising 200K hypothesis-transcription pairs spanning diverse age groups and speaking styles. Results demonstrate that fine-tuning on the CHSER dataset achieves up to a 28.5% relative WER reduction in a zero-shot setting and a 13.3% reduction when applied to fine-tuned ASR systems. Additionally, our error analysis reveals that while GenSEC improves substitution and deletion errors, it struggles with insertions and child-specific disfluencies. These findings highlight the potential of GenSEC for improving child ASR.
ATMM-SAGA: Alternating Training for Multi-Module with Score-Aware Gated Attention SASV system
Authors: Amro Asali, Yehuda Ben-Shimol, Itshak Lapidot
Date: 2025-05-23 00:00:00
Tasks: Speaker Verification

The objective of automatic speaker verification (ASV) systems is to determine whether a given test speech utterance corresponds to a claimed enrolled speaker. These systems have a wide range of applications, and ensuring their reliability is crucial. In this paper, we propose a spoofing-robust automatic speaker verification (SASV) system employing a score-aware gated attention (SAGA) fusion scheme, integrating scores from a pre-trained countermeasure (CM) with speaker embeddings from a pre-trained ASV. Specifically, we employ the AASIST and ECAPA-TDNN models. SAGA acts as an adaptive gating mechanism, where the CM score determines how strongly ASV embeddings influence the final SASV decision. Experiments on the ASVspoof2019 logical access dataset demonstrate that the proposed SASV system achieves an SASV equal error rate (SASV-EER) and agnostic detection cost function (a-DCF) of 2.31%, 0.0603 for the development set and 2.18%, 0.0480 for the evaluation set.
Voicing Personas: Rewriting Persona Descriptions into Style Prompts for Controllable Text-to-Speech
Authors: Yejin Lee, Jaehoon Kang, Kyuhong Shim
Date: 2025-05-21 00:00:00
Tasks: text-to-speech, Text to Speech

In this paper, we propose a novel framework to control voice style in prompt-based, controllable text-to-speech systems by leveraging textual personas as voice style prompts. We present two persona rewriting strategies to transform generic persona descriptions into speech-oriented prompts, enabling fine-grained manipulation of prosodic attributes such as pitch, emotion, and speaking rate. Experimental results demonstrate that our methods enhance the naturalness, clarity, and consistency of synthesized speech. Finally, we analyze implicit social biases introduced by LLM-based rewriting, with a focus on gender. We underscore voice style as a crucial factor for persona-driven AI dialogue systems.
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and Optimization
Authors: Yezhi Shen, Qiuchen Zhai, Fengqing Zhu
Date: 2025-05-28 00:00:00
Tasks: 3D geometry, 3D Reconstruction, Data Augmentation, Neural Rendering, Video Frame Interpolation

Neural rendering methods have gained significant attention for their ability to reconstruct 3D scenes from 2D images. The core idea is to take multiple views as input and optimize the reconstructed scene by minimizing the uncertainty in geometry and appearance across the views. However, the reconstruction quality is limited by the number of input views. This limitation is further pronounced in complex and dynamic scenes, where certain angles of objects are never seen. In this paper, we propose to use video frame interpolation as the data augmentation method for neural rendering. Furthermore, we design a lightweight yet high-quality video frame interpolation model, PS4PRO (Pixel-to-pixel Supervision for Photorealistic Rendering and Optimization). PS4PRO is trained on diverse video datasets, implicitly modeling camera movement as well as real-world 3D geometry. Our model performs as an implicit world prior, enriching the photo supervision for 3D reconstruction. By leveraging the proposed method, we effectively augment existing datasets for neural rendering methods. Our experimental results indicate that our method improves the reconstruction performance on both static and dynamic scenes.
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method
Authors: Alanna Hazlett, Naomi Ohashi, Timothy Rodriguez, Sodiq Adewole
Date: 2025-05-28 00:00:00
Tasks: Classification, Transfer Learning

In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, pneumonia, tuberculosis (TB), and normal cases. We leveraged transfer learning techniques with state-of-the-art pre-trained Convolutional Neural Networks (CNNs) models. We fine-tuned these pre-trained architectures on a labeled medical x-ray images. The initial results are promising with high accuracy and strong performance in key classification metrics such as precision, recall, and F1 score. We applied Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability to provide visual explanations for classification decisions, improving trust and transparency in clinical applications.