GenAI4ELab/papercli-papers huggingface dataset:
Investigating effects of sex hormones, cycle phases and age on female fundamental frequency
Authors: Melanie Weirich; Adrian Simpson
Venue: Interspeech 2025 (main)

Abstract: Mean fundamental frequency (f0) has been found to be affected by hormones. In females, studies show contradicting results regarding the influence of hormonal changes across the cycle on f0. Some studies have suggested hormonal changes due to menopause are responsible for the decrease in female f0 with age, while others point to an earlier onset of this decrease.Here, the effect of hormones (estradiol, progesterone, testosterone) on mean f0 within speakers across the cycle and between speakers was investigated in 62 German participants. In addition, the factors age, height and speech material were included.Results reveal that mean f0 was not affected by cycle phase. Also, variation in mean f0 across speakers could not be explained by variation in investigated hormones. In contrast, a clear effect of speakers' age on mean f0 was found, irrespective of hormone levels, height and speech material, pointing to other factors contributing to a lower mean f0 with increasing age in females.
Pre-aspiration in Iceland Is Conditioned by Gender/Sex
Authors: Meike Rommel; MÃ­Å¡a HejnÃ¡; Nicole DehÃ©
Venue: Interspeech 2025 (main)

Abstract: This paper focuses on pre-aspiration, defined here as a period of (primarily) glottal friction found in sequences of vocalic and consonantal sonorants and phonetically voiceless obstruents. Previous pre-aspiration studies revealed sensitivity of the phenomenon to (bio)social factors. However, there is little work that actually focuses on sociophonetic effects on pre-aspiration. One of the most common findings in the sociophonetics of pre-aspiration is that female speakers are more likely to pre-aspirate more often and with longer durations. Our study asks whether pre-aspiration is conditioned by gender/sex in Icelandic, assumed to be one of the prototypical pre-aspirating languages. We analyse 20 speakers of Modern Icelandic participating in a picture naming task. Our results show that female speakers produce longer durations of local breathiness and voiceless pre-aspiration, as well as noisier tokens.
Transcribing Diverse Voices: Using Whisper for ICE corpora
Authors: Andreas Weilinghoff
Venue: Interspeech 2025 (main)

Abstract: The precise transcription of speech data is crucial yet work-intensive in the field of sociolinguistics. Although recent advancements in end-to-end ASR (e.g. Whisper) offer great potential across various disciplines, these models have rarely been tested for sociolinguistic corpus transcription. This study addresses this gap by harnessing all Whisper models for the re-transcription of classic sociolinguistic reference corpora of non-standard varieties: ICE Nigeria and ICE Scotland. Employing WER metrics, the study utilizes linear mixed-effects modelling to determine significant factors affecting transcription accuracy. The results show that Whisper can manage both varieties, though it is slightly less accurate for Nigerian English. An increased model size reduces WER and boosts robustness, though accuracy varies by sound file. While Whisper proves useful for corpus transcription work overall, challenges such as speaker diarization, hallucinations and idealized transcriptions persist.
Is it all about race?: A Cross-examination of /s/ in a Multilingual (Nigerian) Context
Authors: Oluwasegun Amoniyan
Venue: Interspeech 2025 (main)

Abstract: Previous studies on /s/ variation have examined how /s/ production functions as an indexical marker, particularly in relation to gender and race. These studies primarily focus on Western contexts (e.g., North America) to explain that /s/ can signal gendered personas and racialized social meanings. However, in a multilingual context like Nigeria, /s/ has not been explored to identify motivation(s) for /s/ variation.This study, therefore, examines /s/ production in Nigerian English (NigE), focusing on the factors that condition /s/ variation. The study samples 4,056 tokens of /s/ from the ICE-NigEand they are analyzed using center of gravity, zero crossings, duration, and skewness to explain /s/ frontness or backness.The results reveal that ethnicity and year of birth influence /s/ variation. The younger speakers produce fronted /s/, and older speakers realize a more retracted /s/. While /s/ is more fronted among the Igbo NigE speakers, it is more retracted among Yoruba and Hausa NigE speakers, and the absence of gender difference may highlight the importance of ethnicity rather than gender in a multilingual context.
Investigating Gender Bias in Text-to-Audio Generation Models
Authors: Aarish Shah Mohsin; Mohammad Nadeem; Shahab Saquib Sohail; Tughrul Arsalan; Mandar Gogate; Nasir Saleem; Amir Hussain
Venue: Interspeech 2025 (main)

Abstract: Text-to-Audio (TTA) generation models have demonstrated significant advancements in generating quality audio content from textual prompts. However, these models may inherit and propagate gender biases present in their training data potentially resulting audio outputs that reinforce harmful stereotypes. To address this concern, we systematically analyzed the presence of gender bias in TTA models by employing a comprehensive taxonomy of gender-associated terms. We utilized three state-of-the-art TTA generation models (AudioGen, AudioLDM and Stable Audio) for generating audio samples and applied a gender identification tool to classify their perceived gender. Furthermore, we proposed a novel metric to quantitatively measure the extent of gender bias in audio outputs. Our findings reveal that TTA models frequently exhibit gender bias, often reflecting existing societal stereotypes. The study highlights the need for robust bias evaluation frameworks in text-to-audio generation systems.
Dual Orthogonality Sub-center Loss for Enhanced Anomalous Sound Detection
Authors: Dong Wang; Jiqing Han; Tieran Zheng; Guibin Zheng; Yongjun He
Venue: Interspeech 2025 (main)

Abstract: Anomalous Sound Detection (ASD) requires modeling a compact and discriminative normal sound distribution. Recently, angular margin loss with multiple sub-centers has been shown to be effective for ASD by extending sub-centers to capture intra-class diversity and maximizing their orthogonality to enhance model discriminability. However, existing methods do not consider that the orthogonality of intra-class sub-centers needs to be optimized based on the inherent data structure to avoid over-extension of the representation space due to over-orthogonality. To address this issue, we propose a Dual Orthogonality Sub-Center Loss (DOSCL) that enforces strict orthogonality of inter-class sub-centers to improve anomaly discrimination while applying relaxed constraints on intra-class sub-centers to capture the data structure. Experiments on the DCASE2023 Challenge Task2 dataset show that DOSCL achieves 1.74% AUC and 0.62% pAUC improvements over a strong baseline, validating its effectiveness.
Adaptive Across-Subcenter Representation Learning for Imbalanced Anomalous Sound Detection
Authors: Dong Wang; Jiqing Han; Guibin Zheng; Tieran Zheng; Yongjun He
Venue: Interspeech 2025 (main)

Abstract: Anomalous Sound Detection requires constructing a distribution using only normal sounds. However, collecting sufficient normal samples across diverse conditions is challenging, leading to sample imbalance within subclasses. Existing subcenter angular margin loss methods use multiple subcenters to capture intra-class diversity but still suffer from under-representation or overfitting. To address this issue, we propose Adaptive Across-Subcenter Representation Learning (AASRL). Unlike existing methods that use either a single or all subcenters, AASRL adaptively selects subcenters based on the representation quality of samples and optimizes their representation across the most relevant subcenters. This ensures efficient representation of each sample and prevents the majority subclass from dominating the representation space. Experiments on the DCASE2023 Challenge Task2 dataset and a constructed imbalanced dataset demonstrate the effectiveness of AASRL.
Towards Few-Shot Training-Free Anomaly Sound Detection
Authors: Ho-Hsiang Wu; Wei-Cheng Lin; Abinaya Kumar; Luca Bondi; Shabnam Ghaffarzadegan; Juan Pablo Bello
Venue: Interspeech 2025 (main)

Abstract: Anomaly sound detection is an important audio task, with various applications in industrial monitoring, healthcare, security and surveillance, and other domains. Existing methods are often designed with various assumptions for specific types of anomalies and are not suitable when applied to the challenging real-world scenarios, such as domain shifts and data scarcity issues. In this work, we propose a few-shot training-free method, leveraging pre-trained audio models to extract patch-based spatial-temporal representations for few-shot anomaly detection and segmentation. We show that our proposed approach is flexible and applicable even under the extreme low-shot regimes, and can at times outperform models trained with the full datasets. Furthermore, our method is more robust in tackling domain shifts, with the need of only few-shot data points to quickly adapt to various conditions, therefore more suitable for deployment in real-world applications.
Finetune Large Pre-Trained Model Based on Frequency-Wise Multi-Query Attention Pooling for Anomalous Sound Detection
Authors: Nan Jiang; Yan Song; Qing Gu; Haoyu Song; Lirong Dai; Ian McLoughlin
Venue: Interspeech 2025 (main)

Abstract: Existing anomalous sound detection (ASD) methods generally rely on the learned patterns of normal sounds to identify deviations as anomalies in generative and discriminative ways. However, partly due to the diverse and sparse distribution of anomalies, they may suffer from poor generalization capability to distinguish unseen normal sounds and anomalies. In this paper, we propose to finetune the large pre-trained model based on frequency-wise multi-query attention pooling (FMQAP) for ASD. Specifically, FMQAP first dynamically fuses multiple output embeddings from the pre-trained model, and then exploits a novel frequency-wise attention pooling scheme to improve the generalization capability for various machine types. Evaluations on DCASE 2023 Task2 benchmark demonstrate the superiority of FMQAP, achieving the state-of-the-art 77.62% in terms of official score.
Acoustic Detection of UAV Abnormality Using One Ground-Based Acoustic Vector Sensor
Authors: Dengjian Zhou; Jianghan Hai; Sijia Liao; Yue Ivan Wu; Kainam Thomas Wong; Xiujuan Zheng
Venue: Interspeech 2025 (main)

Abstract: Defective unmanned aerial vehicles (UAVs), like other malfunctioning machinery, often emit abnormal sounds. Early detection of such acoustic abnormalities enables timely countermeasures to prevent equipment loss and human casualties. This paper proposes a deep learning scheme to passively detect abnormal sounds emitted from UAVs, using only a single ground-based acoustic vector sensor (AVS). This is because lone AVS measured sound intensity spectrogram can already provide information on both the sound sourceâs polar-azimuthal direction and abnormality time-frequency signature. This paper integrates a "convolutional autoencoder" with an "attention module", for UAV abnormality detection. Field experiments, with a UAV in flight and a single AVS on the ground, validate the effectiveness of the proposed method, achieving an F1-score (a machine-learning accuracy metric) exceeding 95% for successful anomaly detection.
StarGAN-Aug: A Cross-domain Fault Audio Generation Method for High-performance Fault Diagnosis of Power Transformers
Authors: Ben Niu; Yangjie Wei; Gang Yang; Yuqiao Wang; Shengling Yu
Venue: Interspeech 2025 (main)

Abstract: Although current data generation methods can enhance the classification performance of existing fault diagnosis models by augmenting the limited training set, their performance improvement is still restricted due to the low quality and insufficient diversity of generated data. To tackle this issue, we propose a cross-domain fault audio generation method based on the improved star generative adversarial networks, namely StarGAN-Aug. In the StarGAN-Aug, the model structure is first optimized using a pre-trained JDC network and a style encoder, leading to efficiently extract the specific frequency in fault signals and generate diverse audio samples across domains. Moreover, we add a comprehensive objective to optimize the model training and enhance its ability to ensure that the generated samples have both high fidelity and diversity. Experimental results show that using our generated audio samples, the classification performance of existing diagnostic models can be significantly improved.
SuPseudo: A Pseudo-supervised Learning Method for Neural Speech Enhancement in Far-field Speech Recognition
Authors: Longjie Luo; Lin Li; Qingyang Hong
Venue: Interspeech 2025 (main)

Abstract: Due to the lack of target speech annotations in real-recorded far-field conversational datasets, speech enhancement (SE) models are typically trained on simulated data. However, the trained models often perform poorly in real-world conditions, hindering their application in far-field speech recognition. To address the issue, we (a) propose direct sound estimation (DSE) to estimate the oracle direct sound of real-recorded data for SE; and (b) present a novel pseudo-supervised learning method, SuPseudo, which leverages DSE-estimates as pseudo-labels and enables SE models to directly learn from and adapt to real-recorded data, thereby improving their generalization capability. Furthermore, an SE model called FARNET is designed to fully utilize SuPseudo. Experiments on the MISP2023 corpus demonstrate the effectiveness of SuPseudo, and our system significantly outperforms the previous state-of-the-art.
Lightweight Front-end Enhancement for Robust ASR via Frame Resampling and Sub-Band Pruning
Authors: Siyi Zhao; Wei Wang; Yanmin Qian
Venue: Interspeech 2025 (main)

Abstract: Recent advancements in automatic speech recognition (ASR) have achieved notable progress, whereas robustness in noisy environments remains challenging. While speech enhancement (SE) front-ends are widely used to mitigate noise as a preprocessing step for ASR, they often introduce computational non-negligible overhead. This paper proposes optimizations to reduce SE computational costs without compromising ASR performance. Our approach integrates layer-wise frame resampling and progressive sub-band pruning. Frame resampling downsamples inputs within layers, utilizing residual connections to mitigate information loss. Simultaneously, sub-band pruning progressively excludes less informative frequency bands, further reducing computational demands. Extensive experiments on synthetic and real-world noisy datasets demonstrate that our system reduces SE computational overhead over 66% compared to the standard BSRNN, while maintaining strong ASR performance.
Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down
Authors: Yingzhi Wang; Anas Alhmoud; Saad Alsahly; Muhammad Alqurishi; Mirco Ravanelli
Venue: Interspeech 2025 (main)

Abstract: OpenAI's Whisper has achieved significant success in Automatic Speech Recognition. However, it has consistently been found to exhibit hallucination issues, particularly in non-speech segments, which limits its broader application in complex industrial settings.In this paper, we introduce a novel method to reduce Whisper's hallucination on non-speech segments without using any pre- or post-possessing techniques. Specifically, we benchmark the contribution of each self-attentional head in the Whisper-large-v3 decoder to the hallucination problem by performing a head-wise mask. Our findings reveal that only 3 of the 20 heads account for over 75% of the hallucinations on the UrbanSound dataset. We then fine-tune these three crazy heads using a collection of non-speech data. The results show that our best fine-tuned model, namely Calm-Whisper, achieves over 80% reduction in non-speech hallucination with only less than 0.1% WER degradation on LibriSpeech test-clean and test-other.
HuBERT-VIC: Improving Noise-Robust Automatic Speech Recognition of Speech Foundation Model via Variance-Invariance-Covariance Regularization
Authors: Hyebin Ahn; Kangwook Jang; Hoirin Kim
Venue: Interspeech 2025 (main)

Abstract: Noise robustness in speech foundation models (SFMs) has been a critical challenge, as most models are primarily trained on clean data and experience performance degradation when the models are exposed to noisy speech. To address this issue, we propose HuBERT-VIC, a noise-robust SFM with variance, invariance, and covariance regularization (VICReg) objectives. These objectives adjust the statistics of noisy speech representations, enabling the model to capture diverse acoustic characteristics and improving the generalization ability across different types of noise. When applied to HuBERT, our model shows relative performance improvements of 23.3% on LibriSpeech test-clean and 13.2% on test-other, compared to the baseline model pre-trained on noisy speech.
MOVER: Combining Multiple Meeting Recognition Systems
Authors: Naoyuki Kamo; Tsubasa Ochiai; Marc Delcroix; Tomohiro Nakatani
Venue: Interspeech 2025 (main)

Abstract: In this paper, we propose Meeting recognizer Output Voting Error Reduction (MOVER), a novel system combination method for meeting recognition tasks. Although there are methods to combine the output of diarization (e.g., DOVER) or automatic speech recognition (ASR) systems (e.g., ROVER), MOVER is the first approach that can combine the outputs of meeting recognition systems that differ in terms of both diarization and ASR. MOVER combines hypotheses with different time intervals and speaker labels through a five-stage process that includes speaker alignment, segment grouping, word and timing combination, etc. Experimental results on the CHiME-8 DASR task and the multi-channel track of the NOTSOFAR-1 task demonstrate that MOVER can successfully combine multiple meeting recognition systems with diverse diarization and recognition outputs, achieving relative tcpWER improvements of 9.55 % and 8.51 % over the state-of-the-art systems for both tasks.
EmbedAug: An Augmentation Scheme for End-to-End Automatic Speech Recognition
Authors: Ashish Panda; Sunil Kumar Kopparapu
Venue: Interspeech 2025 (main)

Abstract: Data augmentation plays a significant role in making automatic speech recognition (ASR) systems robust against unseen test data. Most of the existing data augmentation techniques are designed to work on the speech features. Augmentation of speech embeddings, within the neural network, e.g., the inputsto encoders, are relatively unexplored. We present a simple yet effective augmentation scheme, EmbedAug, which works by replacing a set of randomly selected speech embeddings by either zeros or Gaussian noise during training. EmbedAug does not require additional data, works online during training and adds very little to the overall computational cost. Using Librispeech 100h, Librispeech 960h and MUCS21 multilingual dataset, we show that the proposed EmbedAug is very effective in improving the robustness of ASR systems. Moreover, EmbedAug can be fine tuned on the development set with the help of just one hyperparameter.
Attention Models and Auditory Transduction Features for Noise Robustness
Authors: Cathal Ã FaolÃ¡in; Andrew Hines
Venue: Interspeech 2025 (main)

Abstract: Human abilities surpass current speech processing systems in complex, noisy environments. While popular inputs for Automatic Speech Recognition (ASR) systems, such as raw acoustic signals and Mel spectrograms, perform well in quiet conditions, their effectiveness declines in noise. A recently developed generative WaveNet-based model emulates human auditory transduction in real time, offering alternative input features through its âIHCogramâ outputs. We investigate these IHCograms across various Signal-to-Noise ratios (SNRs) using state-of-the-art feature encoders. Our findings show that IHCograms significantly enhance phoneme recognition in noisy conditions with minimal computational overhead, regardless of the model encoder used. Additionally, we introduce our Attention Feature Encoder (AFE) models, which leverage the channel structure of IHCograms and demonstrate superior size and performance compared to existing feature encoders.
Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform
Authors: Xiangzhu Kong; Hao Huang; Zhijian Ou
Venue: Interspeech 2025 (main)

Abstract: This paper presents SHTNet, a lightweight spherical harmonic transform (SHT) based framework, which is designed to address cross-array generalization challenges in multi-channel automatic speech recognition (ASR) through three key innovations. First, SHT based spatial sound field decomposition converts microphone signals into geometry-invariant spherical harmonic coefficients, isolating signal processing from array geometry. Second, the Spatio-Spectral Attention Fusion Network (SSAFN) combines coordinate-aware spatial modeling, refined self-attention channel combinator, and spectral noise suppression without conventional beamforming. Third, Rand-SHT training enhances robustness through random channel selection and array geometry reconstruction. The system achieves 39.26% average CER across heterogeneous arrays (e.g., circular, square, and binaural) on datasets including Aishell-4, Alimeeting, and XMOS, with 97.1% fewer computations than conventional neural beamformers.
On the Design of a Robust Superdirective Beamformer and Topology Parameter Optimization with Frustum-Shaped Microphone Arrays Featuring Multiple Rings
Authors: Kunlong Zhao; Gongping Huang; Xudong Zhao; Jingdong Chen; Jacob Benesty; Zoran Cvetkovic
Venue: Interspeech 2025 (main)

Abstract: Superdirective beamformers using concentric circular microphone arrays are widely used in various applications for their steering capabilities, high directional gains, and robustness. However, their performance often degrades when the beamformer is steered outside the sensor plane, a limitation common in real-world scenarios due to the array's planar configuration. To address this challenge, this paper introduces a novel class of co-axial frustum-shaped concentric circular microphone arrays, where multiple rings are distributed across different horizontal planes. A robust superdirective beamformer is designed by converting beamforming optimization into a quadratic eigenvalue problem. Additionally, an approach is proposed to optimize the array topology parameters using the particle swarm optimization algorithm. Simulation results demonstrate that the proposed method significantly improves beampattern steering and directivity, while effectively controlling the white noise gain.
Efficient Neural and Numerical Methods for High-QualityOnline Speech Spectrogram Inversion via Gradient Theorem
Authors: Andres Fernandez; Juan Azcarreta Ortiz; ÃaÄdaÅ Bilen; Jesus Monge Alvarez
Venue: Interspeech 2025 (main)

Abstract: Recent work in online speech spectrogram inversion effectively combines Deep Learning with the Gradient Theorem to predict phase derivatives directly from magnitudes. Then, phases are estimated from their derivatives via least squares, resulting in a high quality reconstruction. In this work, we introduce three innovations that drastically reduce computational cost, while maintaining high quality: Firstly, we introduce a novel neural network architecture with just 8k parameters, 30 times smaller than previous state of the art. Secondly, increasing latency by 1 hop size allows us to further halve the cost of the neural inference step. Thirdly, we propose a linear-complexity solver for the least squares step that leverages tridiagonality and positive-semidefiniteness, achieving a speedup of several orders of magnitude. We release samples online.