GenAI4ELab/papercli-papers huggingface dataset:
AfriHuBERT: A self-supervised speech representation model for African languages
Authors: Jesujoba O. Alabi; Xuechen Liu; Dietrich Klakow; Junichi Yamagishi
Venue: Interspeech 2025 (main)

Abstract: In this work, we present AfriHuBERT, an extension of mHuBERT-147, a compact self-supervised learning (SSL) model pretrained on 147 languages. While mHuBERT-147 covered 16 African languages, we expand this to 1,226 through continued pretraining on 10K+ hours of speech data from diverse sources, benefiting an African population of over 600M. We evaluate AfriHuBERT on two key speech tasks, Spoken Language Identification (SLID) and Automatic Speech Recognition (ASR), using the FLEURS benchmark. Our results show a +3.6% F1 score improvement for SLID and a -2.1% average Word Error Rate (WER) reduction for ASR over mHuBERT-147, and demonstrates competitiveness with larger SSL models such as MMS and XEUS. Further analysis shows that ASR models trained on AfriHuBERT exhibit improved cross-corpus generalization and are competitive in extremely low-resource ASR scenarios.
The Faetar Speech Recognition Benchmark
Authors: Michael Ong; Sean Robertson; Leo Peckham; Alba Jorquera Jimenez de Aberasturi; Paula Arkhangorodsky; Robin Huo; Aman Sakhardande; Mark Hallap; Naomi Nagy; Ewan Dunbar
Venue: Interspeech 2025 (main)

Abstract: We introduce the Faetar Automatic Speech Recognition Benchmark, a benchmark corpus designed to push the limits of current approaches to low-resource speech recognition. Faetar, a  Franco-ProvenÃ§al variety spoken primarily in Italy, has no standard orthography, has virtually no existing textual or speech resources other than what is included in the benchmark, and is quite different from other forms of Franco-ProvenÃ§al. The corpus comes from field recordings, most of which are noisy, for which only 5 hours have matching transcriptions, and for which transcriptions are inconsistent. The corpus contains an additional 20 hours of unlabelled speech. We report baseline results from multilingual speech foundation models with a best phone error rate of 30.5%, using a pipeline that continues pre-training on the foundation model using the unlabelled set.
LHCP-ASR: An English Speech Corpus of High-Energy Particle Physics Talks for Narrow-Domain ASR Benchmarking
Authors: Jaume SantamarÃ­a-JordÃ; Pablo Segovia-MartÃ­nez; GonÃ§al V. GarcÃ©s DÃ­az-MunÃ­o; Joan Albert Silvestre-CerdÃ; AdriÃ  GimÃ©nez; RubÃ©n Gaspar Aparicio; RenÃ© FernÃ¡ndez SÃ¡nchez; Jorge Civera; Albert Sanchis; Alfons Juan
Venue: Interspeech 2025 (main)

Abstract: We present LHCP-ASR, an English speech corpus of high-energy particle physics talks, with 235 hours of transcribed speeches extracted from the 2020-2022 Large Hadron Collider Physics (LHCP) conferences, plus 1.5G tokens of in-domain text extracted from scientific documents. About 30 hours of conference talks were manually transcribed to build two reliable tasks for narrow-domain ASR benchmarking. The remaining conference talks (205 hours) were pseudo-labelled using a very competitive in-domain ASR system, in order to build a dataset for training or adaptation purposes. This paper describes the creation of this dataset, and provides first reference WER% figures using OpenAI's Whisper models and our in-domain ASR system, achieving 13.6% and 15.0% WER points on the two test sets. This corpus is publicly released under an open licence. We believe it will fulfil the need in the area of having new open, reliable, real-life and challenging ASR benchmarks.
Towards High-Quality LLM-Based Data for French Spontaneous Speech Simplification: an Exo-Refinement Approach
Authors: LucÃ­a Ormaechea; Nikos Tsourakis; Pierrette Bouillon; Benjamin Lecouteux; Didier Schwab
Venue: Interspeech 2025 (main)

Abstract: This study explores the synthetic data generation capabilities of LLMs for French spontaneous speech simplification (S3), a low-resource NLP task. We introduce the exo-refinement approach, which builds on the self-reflect workflow but differs by using separate models for generation and evaluation. To address the limitations of single-model refinement, it integrates external feedback from distinct LLMs as judges, refining outputs based on 3 task-specific dimensions. Comparing expert-based simplifications from gold transcriptions to LLM synthetic outputs, results show that mistral-large outperforms all benchmarked models, including the MUSS baseline, and mistral-small achieves competitive performance with few refinements. SARI results confirm that iterations improve simplicity gain without compromising semantic meaning, as shown by COMET scores. These findings support exo-refinement as a scalable method for synthetic data generation and future S3 model development.
BR-ASR: Efficient and Scalable Bias Retrieval Framework for Contextual Biasing ASR in Speech LLM
Authors: Xun Gong; Anqi Lv; Wangyou Zhang; Zhiming Wang; Huijia Zhu; Yanmin Qian
Venue: Interspeech 2025 (main)

Abstract: While speech large language models (SpeechLLMs) have advanced standard automatic speech recognition (ASR), contextual biasing for named entities and rare words remains challenging, especially at scale. To address this, we propose BR-ASR: a Bias Retrieval framework for large-scale contextual biasing (up to 200k entries) via two innovations: (1) speech-and-bias contrastive learning to retrieve semantically relevant candidates; (2) dynamic curriculum learning that mitigates homophone confusion which negatively impacts the final performance. The is a general framework that allows seamless integration of the retrieved candidates into diverse ASR systems without finetuning. Experiments on LibriSpeech test-clean/-other achieve state-of-the-art (SOTA) biased word error rates (B-WER) of 2.8%/7.1% with 2000 bias words, delivering 45% relative improvement over prior methods. BR-ASR also demonstrates high scalability: when expanding the bias list to 200k where traditional methods generally fail, it induces only 0.3 / 2.9% absolute WER/ B-WER degradation with a 99.99% pruning rate and only 20ms latency per query on test-other.
SPGISpeech 2.0: Transcribed multi-speaker financial audio for speaker-tagged transcription
Authors: Raymond Grossman; Taejin Park; Kunal Dhawan; Andrew Titus; Sophia Zhi; Yulia Shchadilova; Weiqing Wang; Jagadeesh Balam; Boris Ginsburg
Venue: Interspeech 2025 (main)

Abstract: We introduce SPGISpeech 2.0, a dataset suitable for speaker-tagged transcription in the financial domain. SPGISpeech 2.0 improves the diversity of applicable modeling tasks while maintaining the core characteristic of the original SPGISpeech dataset: audio snippets and their corresponding fully formatted text transcriptions, usable for end-to-end automatic speech recognition (ASR). SPGISpeech 2.0 consists of 3,780 additional hours of professionally transcribed earnings calls. Furthermore, the dataset contains call and speaker information for each audio snippet facilitating multi-talker ASR. We validate the utility of SPGISpeech 2.0 through improvements in speaker-tagged ASR performance of popular speech recognition models after fine-tuning on SPGISpeech 2.0. Released free for non-commercial use, we expect SPGISpeech 2.0 to foster advancements in speech recognition technologies and inspire a wide range of research applications.
Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use
Authors: Titouan Parcollet; Yuan Tseng; Shucong Zhang; Rogier C. van Dalen
Venue: Interspeech 2025 (main)

Abstract: Automatic speech recognition (ASR) research is driven by the availability of common datasets between industrial researchers and academics, encouraging comparisons and evaluations. LibriSpeech, despite its long success as an ASR benchmark, is now limited by its size and focus on clean, read speech, leading to near-zero word error rates. More recent datasets, including MOSEL, YODAS, Gigaspeech, OWSM, Libriheavy or People's Speech suffer from major limitations including licenses that researchers in the industry cannot use, unreliable transcriptions, incorrect audio data, or the lack of evaluation sets. This work presents the Loquacious Set, a 25,000-hour curated collection of commercially usable English speech. Featuring hundreds of thousands of speakers with diverse accents and a wide range of speech types (read, spontaneous, talks, clean, noisy), the Loquacious Set is designed to work for academics and researchers in the industry to build ASR systems in real-world scenarios.
CEREALES : a new dataset of Quebec French accented speech with applications to speech recognition
Authors: Lucas Maison; Thomas Soulas; Marie-Jean Meurs
Venue: Interspeech 2025 (main)

Abstract: Automatic speech recognition (ASR) systems have become widespread and have broad categories of usage and users. It is therefore crucial for ASR models to be robust to speaker variations, particularly accents. Since ASR models perform best on their domain of training, researchers try to improve them by gathering large quantities of accented speech data. However, the availability of such data is often very limited, especially for languages other than English. In this work, we introduce a new corpus of Quebec-accented French that we call the CEREALES dataset. It is a large, gender-balanced corpus of more than 600 hours of spontaneous speech collected during a commission of inquiry in Quebec. We clean the transcripts and align them with the audio, making the dataset useful for both pre-training and fine-tuning models. We share it for the benefit of the research community. In addition, we run ASR experiments and show how this dataset can help address the challenges of accented ASR.
A Semantic Information-based Hierarchical Speech Enhancement Method Using Factorized Codec and Diffusion Model
Authors: Yang Xiang; Canan Huang; Desheng Hu; Jingguang Tian; Xinhui Hu; Chao Zhang
Venue: Interspeech 2025 (main)

Abstract: Most current speech enhancement (SE) methods recover clean speech from noisy inputs by directly estimating time-frequency masks or spectrums. However, these approaches often neglect the distinct attributes, such as semantic content and acoustic details, inherent in speech signals, which can hinder performance in downstream tasks. Moreover, their effectiveness tends to degrade in complex acoustic environments. To overcome these challenges, we propose a novel, semantic information-based, step-by-step factorized SE method using factorized codec and diffusion model. Unlike traditional SE methods, our hierarchical modeling of semantic and acoustic attributes enables more robust clean speech recovery, particularly in challenging acoustic scenarios. Moreover, this method offers further advantages for downstream TTS tasks. Experimental results demonstrate that our algorithm not only outperforms SOTA baselines in terms of speech quality but also enhances TTS performance in noisy environments.
Voice-ENHANCE: Speech Restoration using a Diffusion-based Voice Conversion Framework
Authors: Kyungguen Byun; Jason Filos; Erik Visser; Sunkuk Moon
Venue: Interspeech 2025 (main)

Abstract: We propose a speech enhancement system that combines speaker-agnostic speech restoration with voice conversion (VC) to obtain a studio-level quality speech signal. While voice conversion models are typically used to change speaker characteristics, they can also serve as a means of speech restoration when the target speaker is the same as the source speaker. However, since VC models are vulnerable to noisy conditions, we have included a generative speech restoration (GSR) model at the front end of our proposed system. The GSR model performs noise suppression and restores speech damage incurred during that process without knowledge about the target speaker. The VC stage then uses guidance from clean speaker embeddings to further restore the output speech. By employing this two-stage approach, we have achieved speech quality objective metric scores comparable to state-of-the-art (SOTA) methods across multiple datasets.
SNR-Aligned Consistent Diffusion for Adaptive Speech Enhancement
Authors: Yonghyeon Jun; Beomjun Woo; Myeonghun Jeong; Namsoo Kim
Venue: Interspeech 2025 (main)

Abstract: Generative models have shown strong performance in speech enhancement, and consistency models further improve both speed and quality. Building upon these improvements, we propose an SNR adaptation framework that dynamically aligns the diffusion timestep with the SNR of the input signal, enhancing robustness in diverse noise conditions. In our framework, the reverse process is conditioned on a diffusion timestep that is adjusted based on the estimated SNR, while the additive Gaussian noise is modulated according to the same SNR estimate. This design enables a continuous SNR-conditioning mechanism in which the diffusion timestep serves as an SNR control parameter, allowing the model to adjust its enhancement process based on the input SNR. Experimental results demonstrate that our proposed framework consistently improves perceptual quality, with even greater improvements observed under challenging SNR conditions, highlighting its effectiveness.
MDDM: A Multi-view Discriminative Enhanced Diffusion-based Model for Speech Enhancement
Authors: Nan Xu; Zhaolong Huang; Xiaonan Zhi
Venue: Interspeech 2025 (main)

Abstract: With the development of deep learning, speech enhancement has been greatly optimized in terms of speech quality. Previous methods typically focus on the discriminative supervised learning or generative modeling, which tends to introduce speech distortions or high computational cost. In this paper, we propose MDDM, a Multi-view Discriminative enhanced Diffusion-based Model. Specifically, we take the features of three domains (time, frequency and noise) as inputs of a discriminative prediction network, generating the preliminary spectrogram. Then, the discriminative output can be converted to clean speech by several inference sampling steps. Due to the intersection of the distributions between discriminative output and clean target, the smaller sampling steps can achieve the competitive performance compared to other diffusion-based methods. Experiments conducted on a public dataset and a real-world dataset validate the effectiveness of MDDM, either on subjective or objective metric.
A Neural Codec Approach for Noise-Robust Bandwidth Expansion
Authors: Xi Liu; Mu Yang; Szu-Jui Chen; John H.L. Hansen
Venue: Interspeech 2025 (main)

Abstract: Neural audio codec technology has recently attracted significant attention in various speech processing tasks due to its efficient quantized latent features. In this work, we introduce a novel approach that leverages a pre-trained neural codec network to perform both speech denoising and bandwidth expansion simultaneously. Specifically, we design a conformer-based deep neural network to predict clean codebook indices, which are then used by the pre-trained audio codec model to generate enhanced and bandwidth-expanded audio. We investigated several strategies for generating the clean indices and compared our approach with state-of-the-art methods on the Valentini-Botinhao noisy test set. Experimental results demonstrate that our method achieves performance comparable to leading approaches in noise-robust bandwidth expansion tasks while offering promising improvements in the quality and intelligibility of narrow-band signals. Audio samples are available.
HWB-Net: A Novel High-Performance and Efficient Hybrid Waveform Bandwidth Extension Method
Authors: Xin Liu; Shulin He; Xueliang Zhang
Venue: Interspeech 2025 (main)

Abstract: Speech Bandwidth Extension (BWE) reconstructs missing high-frequency components in narrowband signals to enhance perceptual quality. Conventional signal-processing methods face performance limitations, while deep-learning approaches require substantial computational resources. To address these issues, we present HWB-Net, a novel Hybrid Waveform Bandwidth network that combines half-wave rectified signals with raw waveforms through a lightweight architecture, with a lightweight architecture, reducing both complexity and cost. Specifically, integrating half-wave rectification into BAE-NET-Lite enhances the modelâs understanding of speech signals, leading to improved subjective and objective results, and replacing the decoder with a simple linear layer plus a weighted Gaussian mixture model (WGMM) significantly cuts down parameters (0.20M) and computational complexity (0.013G/s). Evaluations demonstrate HWB-Netâs competitive performance and practical viability for real-time communication through balanced efficiency-accuracy tradeoffs.
Frequency-Domain Enhanced Extreme Bandwidth Extension Network with ICCRN for Superior Speech Quality
Authors: Hongtao Bao; Xueliang Zhang
Venue: Interspeech 2025 (main)

Abstract: Bandwidth extension is a critical task in speech processing that enhances narrowband audio quality by reconstructing essential high-frequency components, which are vital for natural and intelligible speech. Recent advancements like EBEN (Extreme Bandwidth Extension Network) achieve notable results with lightweight generators and real-time operations. However, down-sampling limitations degrade frequency-space information, causing speech distortion as up-sampling struggles to recover lost details. To address this, we propose replacing the generator with an ICCRN in the frequency domain. Leveraging cepstral-domain features and inplace convolutions, our approach mitigates information loss during down-sampling and preserves global spectral structure. Evaluations on the French LibriSpeech dataset show significant improvements, including clearer spectrograms, fewer generator parameters, and lower computational costs, advancing the state of the art in bandwidth extension.
QUADS: Quantized Distillation Framework for Efficient Speech Language Understanding
Authors: Subrata Biswas; Mohammad Nur Hossain Khan; Bashima Islam
Venue: Interspeech 2025 (main)

Abstract: Spoken Language Understanding (SLU) systems must bal ance performance and efficiency, particularly in resource-constrained environments. Existing methods apply distillation and quantization separately, leading to suboptimal compression as distillation ignores quantization constraints. We propose QUADS, a unified framework that optimizes both through multi-stage training with a pre-trained model, enhancing adaptability to low-bit regimes while maintaining accuracy. QUADS achieves 71.13% accuracy on SLURP and 99.20% on FSC, with only minor degradations of up to 5.56% compared to state-of-the-art models. Additionally, it reduces computational complexity by 60-73Ã (GMACs) and model size by 83-700Ã, demonstrating strong robustness under extreme quantization. These results establish QUADS as a highly efficient solution for real-world, resource-constrained SLU applications.
Spoken Language Understanding on Unseen Tasks With In-Context Learning
Authors: Neeraj Agrawal; Sriram Ganapathy
Venue: Interspeech 2025 (main)

Abstract: Spoken language understanding (SLU) tasks involve diverse skills that probe the information extraction, classification and/or generation capabilities of models. In this setting, task-specific training data may not always be available. While traditional task-specific SLU models are unable to cater to such requirements, the speech-text large language models (LLMs) offer a promising alternative with emergent abilities. However, out-of-the-box, our evaluations indicate that the zero/few-shot performance of prominent open-source speech-text LLMs on SLU tasks are not up to the mark. In this paper, we introduce a novel approach to robust task-agnostic fine-tuning using randomized class labels. With this proposed fine-tuning, we illustrate that the performance of the speech-text LLMs on an unseen task is significantly improved over standard approaches. Critically, the proposed approach avoids the requirement of task-specific data annotations for enabling new tasks in speech-text LLMs.
Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning
Authors: Pierre Lepagnol; Sahar Ghannay; Thomas Gerald; Christophe Servan; Sophie Rosset
Venue: Interspeech 2025 (main)

Abstract: Understanding user queries is fundamental in many applications, such as home assistants, booking systems, or recommendations. Accordingly, it is crucial to develop accurate Spoken Language Understanding (SLU) approaches to ensure the reliability of the considered system. Current State-of-the-Art SLU techniques rely on large amounts of training data; however, only limited annotated examples are available for specific tasks or languages.In the meantime, instruction-tuned large language models (LLMs) have shown exceptional performance on unseen tasks in a few-shot setting when provided with adequate prompts. In this work, we propose to explore example selection by leveraging Information retrieval (IR) approaches to build an enhanced prompt that is applied to an SLU task. We evaluate the effectiveness of the proposed method on several SLU benchmarks. Experimental results show that lexical IR methods significantly enhance performance without increasing prompt length.
Modeling Multi-Turn Spoken Language Understanding with Dynamic Graph Convolutional Networks
Authors: Yi Huang; Si Chen; Jingyu Yao; Junlan Feng
Venue: Interspeech 2025 (main)

Abstract: Spoken Language Understanding (SLU) stands as a pivotal element within task-oriented dialogue systems, where it leverages the dialogue context to steer intent detection at the utterance level and slot filling at the token level. Nonetheless, the challenge of judiciously assimilating dialogue context into multi-turn SLU persists as a formidable hurdle. This difficulty is compounded by the inherently dynamic distribution of conversational information in real-world settings, where key details, such as shifts in intent and behavior, can be overshadowed by a deluge of less critical data. To address this issue, we introduce a novel SLU model tailored for intent detection and slot filling in multi-turn interactions. Central to our approach is the incorporation of a dynamic graph convolutional network that selectively amalgamates essential historical information into the dialogue context, thereby enhancing the model's sensitivity to contextually relevant cues. We subject our model to rigorous evaluation on three widely recognized benchmark datasets: SIM, CMCC and FewJoint. The experimental outcomes underscore the model's superior performance, and further results from real-scene datasets strengthen the effectiveness of the method.
DRI-GAN: A Novel Dual Real Input GAN with Triplet Loss for Cross-Lingual and Noisy SLU
Authors: Ankit Kumar; Munir Georges
Venue: Interspeech 2025 (main)

Abstract: Intent detection is a critical task in building spoken language understanding (SLU) systems. We propose a novel semi-supervised Dual Real Input Generative Adversarial Network (DRI-GAN) with triplet loss to enhance the performance of this task. This method effectively leverages both labeled and unlabeled data to achieve superior representation learning. We extract and fuse text embeddings from three locally deployed pre-trained Large Language Models (LLMs) and adapt these embeddings for training the DRI-GAN with triplet loss. Our experiments demonstrate three key findings: (i) In noisy SLU environments, the proposed method outperforms the state-of-the-art by +1.44%. (ii) In zero-shot cross-lingual scenarios, our approach yields substantial accuracy improvements, achieving an absolute gain of 4.19% on MultiATIS++ dataset and 14.60% on MASSIVE dataset. (iii) it achieves higher accuracy without fine-tuning, significantly reducing computational load.