GenAI4ELab/papercli-papers huggingface dataset:
From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology
Authors: Haoyang Li; Yuchen Hu; Chen Chen; Sabato Marco Siniscalchi; Songting Liu; Eng Siong Chng
Venue: Interspeech 2025 (main)

Abstract: Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures needed for high-fidelity SE. Group-Rational KAN (GR-KAN), a variant of Kolmogorov-Arnold Networks (KAN), retains KANâs expressiveness while improving scalability on complex tasks. We adapt GR-KAN to existing DNN-based SE by replacing dense layers with GR-KAN layers in the time-frequency (T-F) domain MP-SENet and adapting GR-KAN's activations into the 1D CNN layers in the time-domain Demucs. Results on Voicebank-DEMAND show that GR-KAN requires up to 4Ã fewer parameters while improving PESQ by up to 0.1. In contrast, KAN, facing scalability issues, outperforms MLP on a small-scale signal modeling task but fails to improve MP-SENet. We demonstrate the first successful use of KAN-based methods for consistent improvement in both time- and SoTA TF-domain SE, establishing GR-KAN as a promising alternative for SE.
Stack Less, Repeat More: A Block Reusing Approach for Progressive Speech Enhancement
Authors: Jangyeon Kim; Ui-Hyeop Shin; Jaehyun Ko; Hyung-Min Park
Venue: Interspeech 2025 (main)

Abstract: 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.
Mamba-based Hybrid Model for Speech Enhancement
Authors: Se-Ha Kim; Tae-Gyeong Kim; Chang-Jae Chun
Venue: Interspeech 2025 (main)

Abstract: In this study, we propose MH-SENet, which is designed for speech enhancement by extracting the temporal and spectral features of speech signals in parallel. MH-SENet, which is based on the U-Net architecture, has an encoder and decoder consisting of a bi-directional Mamba and processes it more precisely by considering all the context of the input sequence. Furthermore, a cross-domain Mamba-Transformer block is constructed between the encoder and decoder to effectively fuse information between each time and frequency domains. We evaluated the performance of our proposed MH-SENet on the VCTK + DEMAND dataset and thus it outperformed existing methods by achieving the highest PESQ score. Despite being a hybrid model, the proposed MH-SENet has a lower number of parameters compared to the conventional models.
Restoring Harmonics: Enhancing Speech Quality with Deep Mask and Harmonic Restoration Network
Authors: Yu Zhao; Zengqiang Shang; Mou Wang; Xin Liu; Pengyuan Zhang
Venue: Interspeech 2025 (main)

Abstract: While speech enhancement models have achieved remarkable progress in noise suppression, critical limitations persist in speech quality improvement. Current methods tend to over-suppress low-energy speech components and suffer from harmonic structure degradation. To tackle these challenges, we propose a two-stage model, Deep Mask and Harmonic Restoration Network (DMHRN). The Deep Mask (DM) stage utilizes a Noise-Reduced Mask (NRM) to aggressively suppress noise. Subsequently, the Harmonic Restoration (HR) stage leverages estimated fundamental frequency (F0) contours to guide the reconstruction of missing harmonic components. Experimental results on the DNS3 dataset demonstrate that DMHRN effectively restores the complete speech structure with fine-grained detail. It significantly outperforms baseline models, with a 6.01% improvement in P.835 OVRL and a 3.67% gain in P.808, achieving a substantial enhancement in speech quality without compromising noise suppression.
GLCLAP: A Novel Contrastive Learning Pre-trained Model for Contextual Biasing in ASR
Authors: Yuxiang Kong; Fan Cui; Liyong Guo; Heinrich Dinkel; Lichun Fan; Junbo Zhang; Jian Luan
Venue: Interspeech 2025 (main)

Abstract: Recently, Automatic Speech Recognition (ASR) that supports prompts has shown remarkable versatility. For contextual biasing with these systems, a pivotal factor lies in obtaining well-matched prompts. To address this issue, Contrastive Language-Audio Pre-training is exploited to retrieve matched entities from a user-specified list. Instead of only confining contrastive learning to the sentence level, we propose the Global-Local Contrastive Language-Audio Pre-trained model (GLCLAP). On the global scale, semantic information is extracted from audio and text, enabling a holistic understanding of the input. On the local scale, detailed local word information of individual segments is focused. This multi-scale information has led to a remarkable improvement in bias word retrieval accuracy. By using the GLCLAP bias word retrieval system as the prompts generation component, the accuracy of the final ASR decoding result is significantly improved without finetuning.
WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing
Authors: Yu Nakagome; Michael Hentschel
Venue: Interspeech 2025 (main)

Abstract: Despite recent advances in end-to-end speech recognition methods, the output tends to be biased to the training dataâs vocabulary, resulting in inaccurate recognition of proper nouns and other unknown terms. To address this issue, we propose a method to improve recognition accuracy of such rare words in CTC-based models without additional training or text-to-speech systems. Specifically, keyword spotting is performed using acoustic features of intermediate layers during inference, and a bias is applied to the subsequent layers of the acoustic model for detected keywords. For keyword detection, we adopt a wildcard CTC that is both fast and tolerant of ambiguous matches, allowing flexible handling of words that are difficult to match strictly. Since this method does not require retraining of existing models, it can be easily applied to even large-scale models. In experiments on Japanese speech recognition, the proposed method achieved a 29% improvement in the F1 score for unknown words.
Ranking and Selection of Bias Words for Contextual Bias Speech Recognition
Authors: Haoxiang Hou; Xun Gong; Wangyou Zhang; Wei Wang; Yanmin Qian
Venue: Interspeech 2025 (main)

Abstract: Contextual Automatic Speech Recognition (ASR) systems have made significant advancements. However, contextual ASR models face challenges when dealing with a large number of bias words. This paper focuses on addressing the limitations of contextual ASR models in handling a substantial number of bias words. First, to guide the model to focus on the most important words, we propose a novel network serving as a scorer for bias word ranking and selection. Second, as an example, we explore the use of the proposed scorer in conjunction with the contextual Whisper model. We create a new bias word list using a named-entity recognition (NER) model, which is closer to real-world scenarios. The results on the LibriSpeech dataset with the IS21 bias words list demonstrate that bias word ranking and selection can significantly enhance the model's performance in recognizing bias words, achieving a relative reduction of over 40% in the Biased Word Error Rate.
OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary
Authors: Yui Sudo; Yusuke Fujita; Atsushi Kojima; Tomoya Mizumoto; Lianbo Liu
Venue: Interspeech 2025 (main)

Abstract: Speech foundation models (SFMs), such as Open Whisper-Style Speech Models (OWSM), are trained on massive datasets to achieve accurate automatic speech recognition. However, even SFMs struggle to accurately recognize rare and unseen words. While contextual biasing (CB) is a promising approach to improve recognition of such words, most CB methods are trained from scratch, resulting in lower performance than SFMs due to the lack of pre-trained knowledge. This paper integrates an existing CB method with OWSM v3.1 while freezing its pre-trained parameters. By leveraging the knowledge embedded in SFMs, the proposed method enables effective CB while preserving the advantages of SFMs, even with a small dataset. Experimental results show that the proposed method improves the biasing word error rate (B-WER) by 11.6 points, resulting in a 0.9 point improvement in the overall WER while reducing the real-time factor by 7.5% compared to the non-biasing baseline on the LibriSpeech 100 test-clean set.
Label-Context-Dependent Internal Language Model Estimation for CTC
Authors: Zijian Yang; Minh-Nghia Phan; Ralf SchlÃ¼ter; Hermann Ney
Venue: Interspeech 2025 (main)

Abstract: Although connectionist temporal classification (CTC) has the label context independence assumption, it can still implicitly learn a context-dependent internal language model (ILM) due to modern powerful encoders. In this work, we investigate the implicit context dependency modeled in the ILM of CTC. To this end, we propose novel context-dependent ILM estimation methods for CTC based on knowledge distillation (KD) with theoretical justifications. Furthermore, we introduce two regularization methods for KD. We conduct experiments on Librispeech and TED-LIUM Release 2 datasets for in-domain and cross-domain evaluation, respectively. Experimental results show that context-dependent ILMs outperform the context-independent priors in cross-domain evaluation, indicating that CTC learns a context-dependent ILM. The proposed label-level KD with smoothing method surpasses other ILM estimation approaches, with more than 13% relative improvement in word error rate compared to shallow fusion.
Assessing the Performance and Efficiency of Mamba ASR in Low-Resource Scenarios
Authors: Rodolfo Zevallos; MartÃ­ Cortada Garcia; Sarah Solito; Carlos Mena; Alex PeirÃ³-Lilja; Javier Hernando
Venue: Interspeech 2025 (main)

Abstract: Mamba, a state space model-based architecture, is emerging as a strong alternative to Transformer models, showing equal or superior performance in sequence generation, including speech. However, analyses have focused mainly on high-resource scenarios. This paper explores Mambaâs potential in ASR for low-resource scenarios. We compare the Transformer-based Conformer and its state-space counterpart, ConMamba, across nine languages with varying training data. Our results show that ConMamba achieves similar WER to Conformer for short-context inputs but significantly improves performance on long-context inputs, reducing WER by up to 50% on average. Additionally, ConMamba enhances efficiency, requiring 40â45% less training time, using 50% less memory, and accelerating inference by 63â70%, making it a more effective ASR solution across different data availability scenarios.
Adapting Whisper for Parameter-efficient Code-Switching Speech Recognition via Soft Prompt Tuning
Authors: Hongli Yang; Yizhou Peng; Hao Huang; Sheng Li
Venue: Interspeech 2025 (main)

Abstract: Large-scale multilingual ASR models like Whisper excel in high-resource settings but face challenges in low-resource scenarios, such as rare languages and code-switching (CS), due to computational costs and catastrophic forgetting. We explore Soft Prompt Tuning (SPT), a parameter-efficient method to enhance CS ASR while preserving prior knowledge. We evaluate two strategies: 1) full fine-tuning (FFT) of both soft prompts and the entire Whisper model, demonstrating improved cross-lingual capabilities compared to traditional methods, and 2) adhering to SPTâs original design by freezing model parameters and only training soft prompts. Additionally, we introduce SPT4ASR, a combination of different SPT variants. Experiments on the SEAME and ASRU2019 datasets show that deep prompt tuning is the most effective SPT approach, and our SPT4ASR methods achieve  further error reductions in CS ASR, maintaining parameter efficiency similar to LoRA, without degrading performance on existing languages.
Mitigating Non-Target Speaker Bias in Guided Speaker Embedding
Authors: Shota Horiguchi; Takanori Ashihara; Marc Delcroix; Atsushi Ando; Naohiro Tawara
Venue: Interspeech 2025 (main)

Abstract: Obtaining high-quality speaker embeddings in multi-speaker conditions is crucial for many applications. A recently proposed guided speaker embedding framework, which utilizes speech activities of target and non-target speakers as clues, drastically improved embeddings under severe overlap with small degradation in low-overlap cases. However, since extreme overlaps are rare in natural conversations, this degradation cannot be overlooked. This paper first reveals that the degradation is caused by the global-statistics-based modules, widely used in speaker feature extractors, being overly sensitive to intervals containing only non-target speakers. As a countermeasure, we propose an extension of such modules that exploit the target speaker activity clues, to compute statistics from intervals where the target is active. The proposed method improves speaker verification performance in both low and high overlap ratios, and diarization performance on multiple datasets.
Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
Authors: Zhaoyang Li; Jie Wang; XiaoXiao Li; Wangjie Li; Longjie Luo; Lin Li; Qingyang Hong
Venue: Interspeech 2025 (main)

Abstract: In speaker diarization, traditional clustering-based methods remain widely used in real-world applications. However, these methods struggle with the complex distribution of speaker embeddings and overlapping speech segments. To address these limitations, we propose an Overlapping Community Detection method based on Graph Attention networks and the Label Propagation Algorithm (OCDGALP). The proposed framework comprises two key components: (1) a graph attention network that refines speaker embeddings and node connections by aggregating information from neighboring nodes, and (2) a label propagation algorithm that assigns multiple community labels to each node, enabling simultaneous clustering and overlapping community detection. Experimental results show that the proposed method significantly reduces the Diarization Error Rate (DER), achieving a state-of-the-art 15.94% DER on the DIHARD-III dataset without oracle Voice Activity Detection (VAD), and an impressive 11.07% with oracle VAD.
Pushing the Limits of End-to-End Diarization
Authors: Samuel J. Broughton; Lahiru Samarakoon
Venue: Interspeech 2025 (main)

Abstract: In this paper, we present state-of-the-art diarization error rates (DERs) on multiple publicly available datasets, including AliMeeting-far, AliMeeting-near, AMI-Mix, AMI-SDM, DIHARD III, and MagicData RAMC. Leveraging EEND-TA, a single unified non-autoregressive model for end-to-end speaker diarization, we achieve new benchmark results, most notably a DER of 14.49% on DIHARD III. Our approach scales pre-training through 8-speaker simulation mixtures, ensuring each generated speaker mixture configuration is sufficiently represented. These experiments highlight that EEND-based architectures possess a greater capacity for learning than previously explored, surpassing many existing diarization solutions while maintaining efficient speeds during inference.
Spatio-Spectral Diarization of Meetings by Combining TDOA-based Segmentation and Speaker Embedding-based Clustering
Authors: Tobias Cord-Landwehr; Tobias Gburrek; Marc Deegen; Reinhold Haeb-Umbach
Venue: Interspeech 2025 (main)

Abstract: We propose a spatio-spectral, combined model-based and data-driven diarization pipeline consisting of TDOA-based segmentation followed by embedding-based clustering. The proposed system requires neither access to multi-channel training data nor prior knowledge about the number or placement of microphones. It works for both a compact microphone array and distributed microphones, with minor adjustments. Due to its superior handling of overlapping speech during segmentation, the proposed pipeline significantly outperforms the single-channel pyannote approach, both in a scenario with a compact microphone array and in a setup with distributed microphones. Additionally, we show that, unlike fully spatial diarization pipelines, the proposed system can correctly track speakers when they change positions.
Selective Channel Attention based Target Speaker Voice Activity Detection for Speaker Diarization under AD-HOC Microphone Array Settings
Authors: Hongyu Zhang; Ming Cheng; Jing Feng; Ming Li
Venue: Interspeech 2025 (main)

Abstract: Speaker diarization benefits from multi-channel microphone arrays, yet current systems struggle with diverse configurations. We address this by simulating a dataset with various microphone topologies and proposing Selective Channel Attention-based Target Speaker Voice Activity Detection (SCA-TSVAD). We utilize cross-channel self-attention with masking mechanisms to enable selective attention on specific channels, allowing for the effective processing of audio data with variable multi-channel configurations. SCA-TSVAD is built upon the foundation of single-channel TSVAD. It performs superior on our simulated dataset, showcasing its robustness across diverse array configurations. To further validate the effectiveness of a real dataset, we evaluate SCA-TSVAD on the real-world Ali-Meeting database, where it successfully handles multi-channel audio inputs even when some channels were unavailable or malfunctioning, proving its practical applicability.
Diarization-Guided Multi-Speaker Embeddings
Authors: Joonas Kalda; ClÃ©ment PagÃ©s; Tanel AlumÃ¤e; HervÃ© Bredin
Venue: Interspeech 2025 (main)

Abstract: Reliable speaker embeddings are critical for multi-speaker speech processing tasks. Traditionally models are trained on single-speaker utterances and suffer from domain mismatch when applied in multi-speaker contexts. Recently proposed guided speaker embeddings (GSE) were shown to improve this by training on synthetic multi-speaker mixtures guided by oracle speaker activity labels. Additionally modeling all speakers present in a chunk is desirable but the performance of such methods has been sub-par up to now. We build on GSE by modeling multiple speakers together and using diarization features for guiding. We also propose a new validation metric for embeddings in multi-speaker context and demonstrate its effectiveness. Results on multiple speaker diarization datasets demonstrate that we improve on speed and performance while reducing the embedding model size.
Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering
Authors: Ivan Medennikov; Taejin Park; Weiqing Wang; He Huang; Kunal Dhawan; Jinhan Wang; Jagadeesh Balam; Boris Ginsburg
Venue: Interspeech 2025 (main)

Abstract: This paper presents a streaming extension for the Sortformer speaker diarization framework, whose key property is the arrival-time ordering of output speakers. The proposed approach employs an Arrival-Order Speaker Cache (AOSC) to store frame-level acoustic embeddings of previously observed speakers. Unlike conventional speaker-tracing buffers, AOSC orders embeddings by speaker index corresponding to their arrival time order, and is dynamically updated by selecting frames with the highest scores based on the model's past predictions. Notably, the number of stored embeddings per speaker is determined dynamically by the update mechanism, ensuring efficient cache utilization and precise speaker tracking. Experiments on benchmark datasets confirm the effectiveness and flexibility of our approach, even in low-latency setups. These results establish Streaming Sortformer as a robust solution for real-time multi-speaker tracking and a foundation for streaming multi-talker speech processing.
A Hybrid Approach to Combining Role Diarization with ASR for Professional Conversations
Authors: Bongjun Kim; Arindam Ghosh; Mark C. Fuhs; Anurag Chowdhury; Deblin Bagchi; Monika Woszczyna
Venue: Interspeech 2025 (main)

Abstract: In professional settings, conversations often involve persons with defined roles (doctor, patient, lawyer, client, etc.), and the intelligibility of a conversational transcript may be improved by annotating conversational turns with the role of the speaker, e.g. "Doctor: How are you feeling? Patient: I sprained my ankle." We propose a novel hybrid architecture that combines an ASR model augmented to label the speaker's role at each speaker change point with a d-vector-based diarization system. This system outperforms modular and fully integrated baselines by 12% and 28%, respectively. We also show that, when an ASR transducer model is trained to predict role or speaker-change tokens as part of the transcript, these token timings can improve diarization more than the adjacent word token timings can, despite there being no explicit training signal conveying precise speaker change points.
An interpretable speech foundation model for depression detection by revealing prediction-relevant acoustic features from long speech
Authors: Qingkun Deng; Saturnino Luz; Sofia de la Fuente Garcia
Venue: Interspeech 2025 (main)

Abstract: Speech-based depression detection tools could aid early screening. Here, we propose an interpretable speech foundation model approach to enhance the clinical applicability of such tools. We introduce a speech-level Audio Spectrogram Transformer (AST) to detect depression using long-duration speech instead of short segments, along with a novel interpretation method that reveals prediction-relevant acoustic features for clinician interpretation. Our experiments show the proposed model outperforms a segment-level AST, highlighting the impact of segment-level labeling noise and the advantage of leveraging longer speech duration for more reliable depression detection. Through interpretation, we observe our model identifies reduced loudness and F0 as relevant depression signals, aligning with documented clinical findings. This interpretability supports a responsible AI approach for speech-based depression detection, rendering such tools more clinically applicable.
Speech and Text Foundation Models for Depression Detection: Cross-Task and Cross-Language Evaluation
Authors: LucÃ­a GÃ³mez-ZaragozÃ¡; Javier MarÃ­n-Morales; Mariano AlcaÃ±iz; Mohammad Soleymani
Venue: Interspeech 2025 (main)

Abstract: Automated depression detection is gaining attention due to its potential to improve psychiatric care. This study compares the performance of foundation models (FMs) in two datasets: an extended Distress Analysis Interview Corpus (DAIC+) in English, and Depressive Indicators during Casual Talks (DEPTALK) dataset in Spanish. HuBERT models and their fine-tuned versions for emotion recognition (ER) are used for speech. RoBERTa models and their ER variants are applied for text. Representations from FMs are grouped into context windows and processed by a Gated Recurrent Unit. Early fusion is used for multimodal analysis. Speech models perform similarly across datasets (F1â0.60). Text models perform better on DAIC+ than on DEPTALK (F1=0.70 vs 0.45). Multimodal models using FMs fine-tuned for ER perform best for both (F1=0.75 in DAIC+, 0.69 in DEPTALK), showing effectiveness across tasks and languages. Fairness evaluation reveals gender bias, which motivates future research on its alleviation.