GenAI4ELab/papercli-papers huggingface dataset:
CAGCRN: Real-Time Speech Enhancement with a Lightweight Model for Joint Acoustic Echo Cancellation and Noise Suppression
Authors: Yuyang Wang; Yonghui Liu; Jianbing Liu; Kai Niu; Zhiqiang He
Venue: Interspeech 2025 (main)

Abstract: The rapid advancement of communication technologies has made acoustic echo cancellation (AEC) and noise suppression (NS) increasingly essential. Most existing research tackles these tasks separately, often cascading models in practical systems, which is not suitable for resource-constrained environments. In contrast, a model that simultaneously addresses both tasks with minimal computational resources can significantly reduce system complexity. Furthermore, traditional AEC systems often encounter challenges with audio signal delays, compromising their effectiveness. This paper proposes the cross-attention gated convolutional recurrent network (CAGCRN), which utilizes a gating mechanism to efficiently collaborate AEC and NS tasks, and a cross-attention mechanism to align delays. Experimental results show that CAGCRN excels in both AEC and NS tasks while requiring minimal computational resources, with only 0.07M parameters, making it ideal for devices with limited capabilities.
Exploiting Echo Path Priors for Enhanced Stereo Acoustic Echo Cancellation
Authors: Jinfu Wang; Ziteng Wang; Xin Liu; Yang Liu; Qing Shi; Zhengqiang Luo; Feiran Yang
Venue: Interspeech 2025 (main)

Abstract: Adaptive filters have long served to model echo paths in stereo acoustic echo cancellation (SAEC). In modern devices such as mobile phones and smart speakers, the fixed geometry of loudspeakers and microphones allows a priori echo paths to be identified. However, this prior knowledge remains underexplored. In this paper, we propose an enhanced multichannel state-space frequency-domain adaptive filtering (MCSSFDAF) algorithm for SAEC, which is informed by a priori echo path energy. By dynamically adjusting the process noise covariance in MCSSFDAF based on tracked misalignment between estimated and prior echo paths, our method achieves faster convergence and lower misalignment. Experiments with both simulated and real-world recordings validate the algorithmâs efficacy, demonstrating accelerated reconvergence during echo path changes and superior performance across diverse scenarios.
Extended Loss: Incorporating Long Context into Training Models when using Short Audio Frames
Authors: Quang Minh Dinh; Hoda Rezaee Kaviani; Mehrdad Hosseinzadeh; Yuanhao Yu
Venue: Interspeech 2025 (main)

Abstract: Recently deep learning solutions have been successfully applied to many signal processing tasks including acoustic echo cancellation (AEC). Most existing works focus on architecture design and ignore practical issues such as the effect of frame length on the performance of end-to-end AEC models. In real-time applications, frame length can be as small as 10ms. Since the observed context is very limited during training, it results in boundary discontinuities (glitches) in the final output. While using long frames or post-processing can help, it adds extra delay which may not be desirable depending on the application. In this paper, we investigate the practical issue of handling short frames for AEC, and propose an efficient remedy. By keeping the long context information in each batch and using it during loss calculation, we compensate for the short frames. Our solution is model-agnostic and does not affect the inference time.
Analysis and Extension of a Near-End Listening Enhancement Method Based on Long-Term Fractile Noise Statistics
Authors: Filippo Villani; Wai-Yip Chan; Zheng-Hua Tan; Jan Ãstergaard; Jesper Jensen
Venue: Interspeech 2025 (main)

Abstract: This paper addresses the problem of near-end listening enhancement (NELE), where a clean speech signal is modified prior to playback and under an energy constraint to improve intelligibility in noise. We analyze a recently proposed NELE method, optimized using a Speech Intelligibility Index that has been modified to incorporate temporal aspects of the noise via long-term fractile noise statistics. Specifically, we explain the energy allocation strategy adopted by the algorithm, and show that, in contrast to many existing methods, the spectral energy distribution of the modified speech is a function of that of the background noise, but not that of the input speech. Our simulation experiments show that this simple method outperforms well-established spectral shaping NELE methods. In addition, we extend the algorithm by appending an off-the-shelf dynamic range compressor, and show that it performs generally better than state-of-the-art methods for NELE.
A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control
Authors: Yuan-Kuei Wu; Juan Azcarreta Ortiz; Kashyap Patel; Buye Xu; Jung-Suk Lee; Sanha Lee; Ashutosh Pandey
Venue: Interspeech 2025 (main)

Abstract: This study presents a deep-learning framework for controlling multichannel acoustic feedback in audio devices. Traditional digital signal processing methods struggle with convergence when dealing with high correlated noise such as feedback. We introduce a Convolutional Recurrent Network that efficiently combines spatial and temporal processing, significantly enhancing speech enhancement capabilities with lower computational demands. Our approach utilizes three training methods: In-a-Loop Training, Teacher Forcing, and a Hybrid strategy with a Multichannel Wiener Filter, optimizing performance in complex acoustic environments. This scalable framework offers a robust solution for real-world applications, making significant advances in Acoustic Feedback Control technology.
Diffusion Buffer: Online Diffusion-based Speech Enhancement with Sub-Second Latency
Authors: Bunlong Lay; Rostilav Makarov; Timo Gerkmann
Venue: Interspeech 2025 (main)

Abstract: Diffusion models are a class of generative models that have been recently used for speech enhancement with remarkable success but are computationally expensive at inference time. Therefore, these models are impractical for processing streaming data in real-time. In this work, we adapt a sliding window diffusion framework to the speech enhancement task. Our approach progressively corrupts speech signals through time, assigning more noise to frames close to the present in a buffer. This approach outputs denoised frames with a delay proportional to the chosen buffer size, enabling a trade-off between performance and latency. Empirical results demonstrate that our method outperforms standard diffusion models and runs efficiently on a GPU, achieving an input-output latency in the order of 0.3 to 1 seconds. This marks the first practical diffusion-based solution for online speech enhancement.
Addressing Task Conflicts in Stuttering Detection via MMoE-Based Multi-Task Learning
Authors: Xiaokang Liu; Xingfeng Li; Yudong Yang; Lan Wang; Nan Yan
Venue: Interspeech 2025 (main)

Abstract: Multi-Task Learning (MTL) is widely used in automatic stuttering detection to identify stuttering symptoms; however, task conflicts can hinder performance. This paper addresses the task conflict issue in MTL-based stuttering detection and proposes a rule-based MTL strategy and a Multi-Mixture-of-Experts (MMoE) MTL framework to alleviate these conflicts. We analyze the inherent conflicts in stuttering detection tasks and develop a rule-based MTL strategy to mitigate them. Additionally, we introduce an MoE-based adaptive multi-task strategy to optimize task allocation. Experimental results show that our approach outperforms current state-of-the-art methods. In the 2024 SLT Stuttering Speech Challenge, the rule-based MTL strategy achieved a 19.9% increase in average F1 score over the baseline, securing first place. The MMoE-MTL strategy further enhanced task collaboration, improving the average F1 score by 7.55%, demonstrating its effectiveness.
Comparison of Acoustic and Textual Features for Dysarthria Severity Classification in Amyotrophic Lateral Sclerosis
Authors: Upendra Vishwanath Y. S.; Tanuka Bhattacharjee; Deekshitha G; Sathvik Udupa; Kumar Chowdam Venkata Thirumala; Madassu Keerthipriya; Darshan Chikktimmegowda; Dipti Baskar; Yamini Belur; Seena Vengalil; Atchayaram Nalini; Prasanta Kumar Ghosh
Venue: Interspeech 2025 (main)

Abstract: We explore language-agnostic deep text embeddings for severity classification of dysarthria in Amyotrophic Lateral Sclerosis (ALS). Speech recordings are transcribed by human and ASR and embeddings of the transcripts are considered. Though speech recognition accuracy has been studied for grading dysarthria severity, no effort has yet been made to utilize text embeddings of the transcripts. We perform severity classification at different granularity (2, 3, and 5-class) using data obtained from 47 ALS subjects. Experiments with dense neural network based classifiers suggest that, though text features achieve nearly equal performances as baseline speech features, like statistics of mel frequency cepstral coefficients (MFCC), for 2-class classification, speech features outperform for higher number of classes. Concatenation of text embeddings and MFCC statistics attains the best performances with mean F1 scores of 88%, 68%, and 53%, respectively, in 2, 3, and 5-class classification.
StutterCut: Uncertainty-Guided Normalised Cut for Dysfluency Segmentation
Authors: Suhita Ghosh; Melanie Jouaiti; Jan-Ole Perschewski; Sebastian Stober
Venue: Interspeech 2025 (main)

Abstract: Detecting and segmenting dysfluencies is crucial for effective speech therapy and real-time feedback. However, most methods only classify dysfluencies at the utterance level. We introduce StutterCut, a semi-supervised framework that formulates dysfluency segmentation as a graph partitioning problem, where speech embeddings from overlapping windows are represented as graph nodes. We refine the connections between nodes using a pseudo-oracle classifier trained on weak (utterance-level) labels, with its influence controlled by an uncertainty measure from Monte Carlo dropout. Additionally, we extend the weakly labelled FluencyBank dataset by incorporating frame-level dysfluency boundaries for four dysfluency types. This provides a more realistic benchmark compared to synthetic datasets. Experiments on real and synthetic datasets show that StutterCut outperforms existing methods, achieving higher F1 scores and more precise stuttering onset detection.
Physiologically-Informed Feature Analysis of Acquired Speech Disorders for Stroke Assessment
Authors: Giulia Sanguedolce; JÃ³n GuÃ°nason; Dragos C. Gruia; Emilie d'Olne; Fatemeh Geranmayeh; Patrick A. Naylor
Venue: Interspeech 2025 (main)

Abstract: Post-stroke speech disorders impair communication and rehabilitation outcomes, often requiring prolonged, intensive therapy sessions. The diversity of symptoms, coupled with the high cost and logistical burden of traditional speech therapy, underscores the need for accurate, automatic assessment to support tailored interventions. Leveraging a purpose-built database of stroke patients, this study introduces a feature-driven framework integrating traditional acoustic features with physiologically informed glottal parameters for classifying impaired speech after stroke. Evaluating unimodal, combined, and SHAP-derived feature configurations, our approach achieved a 97% F1-score in distinguishing pathological from healthy speech. These results highlight the potential of combining clinically meaningful glottal and acoustic information to support early speech deterioration detection, enhancing accessibility and personalised rehabilitation strategies for improved patient outcomes.
Robot-assisted Recognition of Vocal Emotions in Pseudospeech for Cochlear Implanted Adolescents
Authors: Gloria Araiza-Illan; Luke Meyer; Bert Maat; Deniz BaÅkent
Venue: Interspeech 2025 (main)

Abstract: Recognising vocal emotions in speech is difficult for children with hearing loss and who use a cochlear implant (CI). As regular monitoring could be burdensome, we propose a NAO robot as a test interface. Adolescents with CIs (10-17yr) performed the EmoHI test for recognising vocal emotions in pseudospeech (no linguistic emotion information), once with a computer and once with a NAO. Interfaces are compared via test results and durations, and participantsâ perception of the interfaces. Test results (sensitivity index, dâ) were similar (0.36 Â± 0.36 vs. 0.37 Â± 0.43), but durations were significantly longer on the NAO (4.18 min Â± 39 sec vs. 5.13 min Â± 51 sec). The computer had a higher perceived usability, but the NAO was rated more enjoyable, engaging and preferable. Overall, the NAO sound quality seems sufficient for conducting the EmoHI test, even with a CI. The higher ratings of enjoyability for NAO may be especially useful in conducting such tests in populations with hearing devices.
Using Neurogram Similarity Index Measure (NSIM) to Model Hearing Loss and Cochlear Neural Degeneration
Authors: Ahsan Cheema; Sunil Puria
Venue: Interspeech 2025 (main)

Abstract: Trouble hearing in noisy situations remains a common complaint for both individuals with hearing loss and individuals with normal hearing. This is hypothesized to arise due to condition called: cochlear neural degeneration (CND) which can also result in significant variabilities in hearing aids outcomes. This paper uses computational models of auditory periphery to simulate various hearing tasks. We present an objective method to quantify hearing loss and CND by comparing auditory nerve fiber responses using a Neurogram Similarity Index Measure (NSIM). Specifically study 1, shows that NSIM can be used to map performance of individuals with hearing loss on phoneme recognition task with reasonable accuracy. In the study 2, we show that NSIM is a sensitive measure that can also be used to capture the deficits resulting from CND and can be a candidate for noninvasive biomarker of auditory synaptopathy.
Contrastive Learning-based Syllable-Level Mispronunciation Detection and Diagnosis for Speech Audiometry
Authors: Longbin Jin; Donghun Min; Jung Eun Shin; Eun Yi Kim
Venue: Interspeech 2025 (main)

Abstract: Speech audiometry assesses hearing disorders, typically relies on audiologists, making the process subjective and requiring in-person evaluation. In this paper, we introduce SylPh, a novel automatic syllable-level mispronunciation detection and diagnosis (MDD) model that generalizes across open-set syllables while also offering phonemic analysis. To capture a wide range of mispronunciation patterns, we construct positive and pseudo-negative bags to extract in-distribution and out-of-distribution features from input audio. Our model aligns audio features with adaptive text embeddings using a contrastive objective, dynamically adjusting decision boundaries for each syllable within a single model. Extensive experiments on a large-scale dataset demonstrate its effectiveness in both closed-set and open-set syllables. Notably, despite training only on syllable-level labels, the Sylph has the capability to localize phoneme-level abnormalities, providing detailed diagnostic insights.
A Deformable Convolution GAN Approach for Speech Dereverberation in Cochlear Implant Users
Authors: Hsin-Tien Chiang; John H.L. Hansen
Venue: Interspeech 2025 (main)

Abstract: Speech dereverberation is crucial for enhancing intelligibility and quality, especially for cochlear implant (CI) users, who are highly susceptible to smearing effects induced by reverberation. While conventional and deep learning-based methods have shown promise for normal-hearing (NH) individuals, their effectiveness for CI users remains limited. To bridge this gap, we propose a deformable convolutional GAN architecture for dereverberation for CI users. The deformable convolution layers introduce kernel offset prediction, adaptively adjusting the receptive field based on distortion in reverberant speech. We first evaluate the effectiveness of the proposed method on REVERB challenge dataset. A listening test is conducted with both NH and CI users. Results show that the proposed method markedly improves speech intelligibility for CI users by preserving a more intact envelope structure, enhancing their ability to perceive key transient speech segments for sentence comprehension.
L3C-DeepMFC: Low-Latency Low-Complexity Deep Marginal Feedback Cancellation with Closed-Loop Fine Tuning for Hearing Aids
Authors: Fengyuan Hao; Brian C. J. Moore; Huiyong Zhang; Xiaodong Li; Chengshi Zheng
Venue: Interspeech 2025 (main)

Abstract: Feedback control in hearing aids mitigates acoustic feedback caused by the coupling between the receiver and microphone. While DNN-based methods have achieved progress, they remain computationally intensive with relatively high latency. This paper introduces L3C-DeepMFC, a low-latency and low-complexity time-frequency (T-F) domain method that employs complex spectrum mapping to estimate the magnitude and phase components of the desired speech. This method integrates full- and sub-band recurrent modeling to capture spectro-temporal patterns and modifies the overlap-add method for low-latency processing. Moreover, we utilize closed-loop fine tuning with dynamically generated feedback mixtures to minimize the mismatch between training and estimation. Evaluations using the AISHELL-3 dataset confirm its competitive performance across various gains, significantly improving the maximum stable gain (MSG). Integration with traditional methods shows better performance of feedback suppression.
Semantic Processing During Spoken Word Production by Children with Cochlear Implants
Authors: Man Wang; Yixin Ding; Niels Schiller
Venue: Interspeech 2025 (main)

Abstract: Research that investigates the speech production of children with cochlear implants (CIs) mostly focuses on the characteristics of their speech sounds. Few studies have looked at the psycholinguistic processes during speech production of children with CI. Our study examines the semantic processing during speech production of this group of speakers, compared to their normal hearing (NH) peers. Using the picture-word interference paradigm, we manipulated the semantic relatedness between target picture names and distractor words. We observed the typical semantic interference effect in the NH group but not in the CI group, suggesting that the semantic network may be organized differently in the CI group than in their NH peers and the CI group may have difficulties accessing the semantic categories. Furthermore, our results are in line with the suggestion that the CI group may rely more on a top-down strategy or attentional cognitive processing than a bottom-up semantic activation.
Linguistic Masking and Its Release in Simulated Electric-acoustic Hearing
Authors: Yuting Ding; Xuefei Wang; Fei Chen
Venue: Interspeech 2025 (main)

Abstract: Combined electric-acoustic stimulation (EAS) with a cochlear implant (CI) and a hearing aid can significantly improve CI userâs speech-in-noise performance, which is noted as combined stimulation advantage. The present study aimed to investigate this advantage in the context of linguistic masking, i.e., when the target speech and masker were from the same or different languages, and how combined stimulation and different linguistic masker affected linguistic release from masking (LRM). Mandarin sentences were mixed with 2-talker babble maskers spoken in Mandarin, Cantonese or English, processed by noise vocoder models simulating CI or EAS hearing, and presented to normal-hearing listeners to recognize. Experimental results showed the combined stimulation advantage under linguistic masking with all three languages. The EAS condition yielded significant difference between the scores of two LRMs (i.e., English vs. Mandarin, and Cantonese vs. Mandarin). Under linguistic masking with Cantonese, there was significant difference between the scores of CI- and EAS-based LRMs. The findings of the present work provided new insights on the benefits for CI users using combined electric-acoustic stimulation.
Lessons Learned from the URGENT 2024 Speech Enhancement Challenge
Authors: Wangyou Zhang; Kohei Saijo; Samuele Cornell; Robin Scheibler; Chenda Li; Zhaoheng Ni; Anurag Kumar; Marvin Sach; Wei Wang; Yihui Fu; Shinji Watanabe; Tim Fingscheidt; Yanmin Qian
Venue: Interspeech 2025 (main)

Abstract: The URGENT 2024 Challenge aims to foster speech enhancement (SE) techniques with great universality, robustness, and generalizability, featuring a broader task definition, large-scale multi-domain data, and comprehensive evaluation metrics. Nourished by the challenge outcomes, this paper presents an in-depth analysis of two key, yet understudied, issues in SE system development: data cleaning and evaluation metrics. We highlight several overlooked problems in traditional SE pipelines: (1) mismatches between declared and effective audio bandwidths, along with label noise even in various "high-quality" speech corpora; (2) lack of both effective SE systems to conquer the hardest conditions (e.g., speech overlap, strong noise / reverberation) and reliable measure of speech sample difficulty; (3) importance of combining multifaceted metrics for a comprehensive evaluation correlating well with human judgment. We hope that this endeavor can inspire improved SE pipeline designs in the future.
Interspeech 2025 URGENT Speech Enhancement Challenge
Authors: Kohei Saijo; Wangyou Zhang; Samuele Cornell; Robin Scheibler; Chenda Li; Zhaoheng Ni; Anurag Kumar; Marvin Sach; Yihui Fu; Wei Wang; Tim Fingscheidt; Shinji Watanabe
Venue: Interspeech 2025 (main)

Abstract: There has been a growing effort to develop universal speech enhancement (SE) to handle inputs with various speech distortions and recording conditions. The URGENT Challenge series aims to foster such universal SE by embracing a broad range of distortion types, increasing data diversity, and incorporating extensive evaluation metrics. This work introduces the Interspeech 2025 URGENT Challenge, the second edition of the series, to explore several aspects that have received limited attention so far: language dependency, universality for more distortion types, data scalability, and the effectiveness of using noisy training data. We received 32 submissions, where the best system uses a discriminative model, while most other competitive ones are hybrid methods. Analysis reveals some key findings: (i) some generative or hybrid approaches are preferred in subjective evaluations over the top discriminative model, and (ii) purely generative SE models can exhibit language dependency.
TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network
Authors: Xiaobin Rong; Dahan Wang; Qinwen Hu; Yushi Wang; Yuxiang Hu; Jing Lu
Venue: Interspeech 2025 (main)

Abstract: Universal speech enhancement aims to handle input speech with different distortions and input formats. To tackle this challenge, we present TS-URGENet, a Three-Stage Universal, Robust, and Generalizable speech Enhancement Network. To address various distortions, the proposed system employs a novel three-stage architecture consisting of a filling stage, a separation stage, and a restoration stage. The filling stage mitigates packet loss by preliminarily filling lost regions under noise interference, ensuring signal continuity. The separation stage suppresses noise, reverberation, and clipping distortion to improve speech clarity. Finally, the restoration stage compensates for bandwidth limitation, codec artifacts, and residual packet loss distortion, refining the overall speech quality. Our proposed TS-URGENet achieves outstanding performance in the Interspeech 2025 URGENT Challenge, ranking 2nd in Track 1.