pwc-archive/papers-with-abstracts huggingface dataset:
Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric
Authors: Mattson Ogg, Caitlyn Bishop, Han Yi, Sarah Robinson
Date: 2025-06-02 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition

Methods for automatically assessing speech quality are critical for many human language technologies. Behavioral ratings provided by human raters (e.g., mean opinion scores; MOS) are considered the gold standard, but they are susceptible to variability between individual raters, cannot easily be generalized across corpora, and are labor-intensive to collect, thus limiting the acoustic challenges they can quantify. Here, we present a new, scalable method for automatically assessing speech quality: the self-supervised speech quality assessment (S3QA) model. First, we processed high quality utterances from multiple speech corpora, using a wide range of acoustic manipulations intended to emulate common sources of quality degradation in the real-world: frequency filtering, reverberation, background noise, and digital compression. Second, we leveraged an existing, pre-trained speech foundation model, WavLM, to computationally derive a self-supervised training target for the level of signal degradation by calculating the cosine distances between the clean and degraded versions of each utterance in the embedding space. Next, we trained a transformer-based model to predict the cosine distance, or degradation index, given only the degraded versions of these utterances. Finally, the trained model was evaluated on unseen test corpora of synthetic mixtures, NISQA, and VOiCES. We show that the S3QA model trained on this task performs well and is aligned with both behavioral ratings (MOS), speech technology performance (automatic speech recognition) and other important features of the held-out data (e.g., microphone distances). This approach provides an automated, scalable method for assessing speech quality across a wide range of acoustic challenges, and could easily be adapted to other use cases where acoustic simulations are available.
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
Authors: Karl El Hajal, Enno Hermann, Sevada Hovsepyan, Mathew Magimai. -Doss
Date: 2025-06-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Rhythm, speech-recognition, Speech Recognition, Voice Conversion

Automatic speech recognition (ASR) systems struggle with dysarthric speech due to high inter-speaker variability and slow speaking rates. To address this, we explore dysarthric-to-healthy speech conversion for improved ASR performance. Our approach extends the Rhythm and Voice (RnV) conversion framework by introducing a syllable-based rhythm modeling method suited for dysarthric speech. We assess its impact on ASR by training LF-MMI models and fine-tuning Whisper on converted speech. Experiments on the Torgo corpus reveal that LF-MMI achieves significant word error rate reductions, especially for more severe cases of dysarthria, while fine-tuning Whisper on converted data has minimal effect on its performance. These results highlight the potential of unsupervised rhythm and voice conversion for dysarthric ASR. Code available at: https://github.com/idiap/RnV
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
Date: 2025-06-02 00:00:00
Tasks: Speech Enhancement

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.
Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion
Authors: Ajinkya Kulkarni, Sandipana Dowerah, Tanel Alumae, Mathew Magimai. -Doss
Date: 2025-06-02 00:00:00
Tasks: Face Swapping, Metric Learning

Audio deepfakes are acquiring an unprecedented level of realism with advanced AI. While current research focuses on discerning real speech from spoofed speech, tracing the source system is equally crucial. This work proposes a novel audio source tracing system combining deep metric multi-class N-pair loss with Real Emphasis and Fake Dispersion framework, a Conformer classification network, and ensemble score-embedding fusion. The N-pair loss improves discriminative ability, while Real Emphasis and Fake Dispersion enhance robustness by focusing on differentiating real and fake speech patterns. The Conformer network captures both global and local dependencies in the audio signal, crucial for source tracing. The proposed ensemble score-embedding fusion shows an optimal trade-off between in-domain and out-of-domain source tracing scenarios. We evaluate our method using Frechet Distance and standard metrics, demonstrating superior performance in source tracing over the baseline system.
SALF-MOS: Speaker Agnostic Latent Features Downsampled for MOS Prediction
Authors: Saurabh Agrawal, Raj Gohil, Gopal Kumar Agrawal, Vikram C M, Kushal Verma
Date: 2025-06-02 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Voice Conversion

Speech quality assessment is a critical process in selecting text-to-speech synthesis (TTS) or voice conversion models. Evaluation of voice synthesis can be done using objective metrics or subjective metrics. Although there are many objective metrics like the Perceptual Evaluation of Speech Quality (PESQ), Perceptual Objective Listening Quality Assessment (POLQA) or Short-Time Objective Intelligibility (STOI) but none of them is feasible in selecting the best model. On the other hand subjective metric like Mean Opinion Score is highly reliable but it requires a lot of manual efforts and are time-consuming. To counter the issues in MOS Evaluation, we have developed a novel model, Speaker Agnostic Latent Features (SALF)-Mean Opinion Score (MOS) which is a small-sized, end-to-end, highly generalized and scalable model for predicting MOS score on a scale of 5. We use the sequences of convolutions and stack them to get the latent features of the audio samples to get the best state-of-the-art results based on mean squared error (MSE), Linear Concordance Correlation coefficient (LCC), Spearman Rank Correlation Coefficient (SRCC) and Kendall Rank Correlation Coefficient (KTAU).
Analyzing the Importance of Blank for CTC-Based Knowledge Distillation
Authors: Benedikt Hilmes, Nick Rossenbach, Ralf Schlüter
Date: 2025-06-02 00:00:00
Tasks: Automatic Speech Recognition, Knowledge Distillation, speech-recognition, Speech Recognition

With the rise of large pre-trained foundation models for automatic speech recognition new challenges appear. While the performance of these models is good, runtime and cost of inference increases. One approach to make use of their strength while retaining efficiency is to distill their knowledge to smaller models during training. In this work, we explore different CTC-based distillation variants, focusing on blank token handling. We show that common approaches like blank elimination do not always work off the shelf. We explore new blank selection patterns as a potential sweet spot between standard knowledge distillation and blank elimination mechanisms. Through the introduction of a symmetric selection method, we are able to remove the CTC loss during knowledge distillation with minimal to no performance degradation. With this, we make the training independent from target labels, potentially allowing for distillation on untranscribed audio data.
Attention Is Not Always the Answer: Optimizing Voice Activity Detection with Simple Feature Fusion
Authors: Kumud Tripathi, Chowdam Venkata Kumar, Pankaj Wasnik
Date: 2025-06-02 00:00:00
Tasks: Action Detection, Activity Detection, Computational Efficiency

Voice Activity Detection (VAD) plays a key role in speech processing, often utilizing hand-crafted or neural features. This study examines the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) and pre-trained model (PTM) features, including wav2vec 2.0, HuBERT, WavLM, UniSpeech, MMS, and Whisper. We propose FusionVAD, a unified framework that combines both feature types using three fusion strategies: concatenation, addition, and cross-attention (CA). Experimental results reveal that simple fusion techniques, particularly addition, outperform CA in both accuracy and efficiency. Fusion-based models consistently surpass single-feature models, highlighting the complementary nature of MFCCs and PTM features. Notably, our best-performing fusion model exceeds the state-of-the-art Pyannote across multiple datasets, achieving an absolute average improvement of 2.04%. These results confirm that simple feature fusion enhances VAD robustness while maintaining computational efficiency.
Zero-Shot Text-to-Speech for Vietnamese
Authors: Thi Vu, Linh The Nguyen, Dat Quoc Nguyen
Date: 2025-06-02 00:00:00
Tasks: text-to-speech, Text to Speech

This paper introduces PhoAudiobook, a newly curated dataset comprising 941 hours of high-quality audio for Vietnamese text-to-speech. Using PhoAudiobook, we conduct experiments on three leading zero-shot TTS models: VALL-E, VoiceCraft, and XTTS-V2. Our findings demonstrate that PhoAudiobook consistently enhances model performance across various metrics. Moreover, VALL-E and VoiceCraft exhibit superior performance in synthesizing short sentences, highlighting their robustness in handling diverse linguistic contexts. We publicly release PhoAudiobook to facilitate further research and development in Vietnamese text-to-speech.
Online Audio-Visual Autoregressive Speaker Extraction
Authors: Zexu Pan, Wupeng Wang, Shengkui Zhao, Chong Zhang, Kun Zhou, Yukun Ma, Bin Ma
Date: 2025-06-02 00:00:00

This paper proposes a novel online audio-visual speaker extraction model. In the streaming regime, most studies optimize the audio network only, leaving the visual frontend less explored. We first propose a lightweight visual frontend based on depth-wise separable convolution. Then, we propose a lightweight autoregressive acoustic encoder to serve as the second cue, to actively explore the information in the separated speech signal from past steps. Scenario-wise, for the first time, we study how the algorithm performs when there is a change in focus of attention, i.e., the target speaker. Experimental results on LRS3 datasets show that our visual frontend performs comparably to the previous state-of-the-art on both SkiM and ConvTasNet audio backbones with only 0.1 million network parameters and 2.1 MACs per second of processing. The autoregressive acoustic encoder provides an additional 0.9 dB gain in terms of SI-SNRi, and its momentum is robust against the change in attention.
WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing
Authors: Yu Nakagome, Michael Hentschel
Date: 2025-06-02 00:00:00
Tasks: Keyword Spotting, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Despite recent advances in end-to-end speech recognition methods, the output tends to be biased to the training data'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.
GigaAM: Efficient Self-Supervised Learner for Speech Recognition
Authors: Aleksandr Kutsakov, Alexandr Maximenko, Georgii Gospodinov, Pavel Bogomolov, Fyodor Minkin
Date: 2025-06-01 00:00:00
Tasks: Automatic Speech Recognition, Language Modeling, Language Modelling, Masked Language Modeling, Self-Supervised Learning, speech-recognition, Speech Recognition

Self-Supervised Learning (SSL) has demonstrated strong performance in speech processing, particularly in automatic speech recognition. In this paper, we explore an SSL pretraining framework that leverages masked language modeling with targets derived from a speech recognition model. We also present chunkwise attention with dynamic chunk size sampling during pretraining to enable both full-context and streaming fine-tuning. Our experiments examine scaling with respect to model size and the amount of data. Using our method, we train the GigaAM family of models, including a state-of-the-art model for Russian speech recognition that outperforms Whisper-large-v3 by 50%. We have released our foundation and ASR models, along with the inference code, under the MIT license as open-source resources to the research community. Available at https://github.com/salute-developers/gigaam.
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations
Authors: Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan, Drishti Singh, Swarup Ranjan Behera, Pailla Balakrishna Reddy, Arun Balaji Buduru, Rajesh Sharma
Date: 2025-06-01 00:00:00
Tasks: Emotion Recognition, Rhythm, Speaker Recognition, Speech Emotion Recognition, Synthetic Speech Detection

In this work, we focus on source tracing of synthetic speech generation systems (STSGS). Each source embeds distinctive paralinguistic features--such as pitch, tone, rhythm, and intonation--into their synthesized speech, reflecting the underlying design of the generation model. While previous research has explored representations from speech pre-trained models (SPTMs), the use of representations from SPTM pre-trained for paralinguistic speech processing, which excel in paralinguistic tasks like synthetic speech detection, speech emotion recognition has not been investigated for STSGS. We hypothesize that representations from paralinguistic SPTM will be more effective due to its ability to capture source-specific paralinguistic cues attributing to its paralinguistic pre-training. Our comparative study of representations from various SOTA SPTMs, including paralinguistic, monolingual, multilingual, and speaker recognition, validates this hypothesis. Furthermore, we explore fusion of representations and propose TRIO, a novel framework that fuses SPTMs using a gated mechanism for adaptive weighting, followed by canonical correlation loss for inter-representation alignment and self-attention for feature refinement. By fusing TRILLsson (Paralinguistic SPTM) and x-vector (Speaker recognition SPTM), TRIO outperforms individual SPTMs, baseline fusion methods, and sets new SOTA for STSGS in comparison to previous works.
Towards Fusion of Neural Audio Codec-based Representations with Spectral for Heart Murmur Classification via Bandit-based Cross-Attention Mechanism
Authors: Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Swarup Ranjan Behera, Priyabrata Mallick, Santanu Roy, Arun Balaji Buduru, Rajesh Sharma
Date: 2025-06-01 00:00:00
Tasks: Rhythm

In this study, we focus on heart murmur classification (HMC) and hypothesize that combining neural audio codec representations (NACRs) such as EnCodec with spectral features (SFs), such as MFCC, will yield superior performance. We believe such fusion will trigger their complementary behavior as NACRs excel at capturing fine-grained acoustic patterns such as rhythm changes, spectral features focus on frequency-domain properties such as harmonic structure, spectral energy distribution crucial for analyzing the complex of heart sounds. To this end, we propose, BAOMI, a novel framework banking on novel bandit-based cross-attention mechanism for effective fusion. Here, a agent provides more weightage to most important heads in multi-head cross-attention mechanism and helps in mitigating the noise. With BAOMI, we report the topmost performance in comparison to individual NACRs, SFs, and baseline fusion techniques and setting new state-of-the-art.
PARROT: Synergizing Mamba and Attention-based SSL Pre-Trained Models via Parallel Branch Hadamard Optimal Transport for Speech Emotion Recognition
Authors: Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish, Swarup Ranjan Behera, Jaya Sai Kiran Patibandla, Arun Balaji Buduru, Rajesh Sharma
Date: 2025-06-01 00:00:00
Tasks: Emotion Recognition, Mamba, Self-Supervised Learning, Speech Emotion Recognition

The emergence of Mamba as an alternative to attention-based architectures has led to the development of Mamba-based self-supervised learning (SSL) pre-trained models (PTMs) for speech and audio processing. Recent studies suggest that these models achieve comparable or superior performance to state-of-the-art (SOTA) attention-based PTMs for speech emotion recognition (SER). Motivated by prior work demonstrating the benefits of PTM fusion across different speech processing tasks, we hypothesize that leveraging the complementary strengths of Mamba-based and attention-based PTMs will enhance SER performance beyond the fusion of homogenous attention-based PTMs. To this end, we introduce a novel framework, PARROT that integrates parallel branch fusion with Optimal Transport and Hadamard Product. Our approach achieves SOTA results against individual PTMs, homogeneous PTMs fusion, and baseline fusion techniques, thus, highlighting the potential of heterogeneous PTM fusion for SER.
From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models
Authors: Asım Ersoy, Basel Mousi, Shammur Chowdhury, Firoj Alam, Fahim Dalvi, Nadir Durrani
Date: 2025-06-01 00:00:00
Tasks: World Knowledge

The emergence of large language models (LLMs) has demonstrated that systems trained solely on text can acquire extensive world knowledge, develop reasoning capabilities, and internalize abstract semantic concepts--showcasing properties that can be associated with general intelligence. This raises an intriguing question: Do such concepts emerge in models trained on other modalities, such as speech? Furthermore, when models are trained jointly on multiple modalities: Do they develop a richer, more structured semantic understanding? To explore this, we analyze the conceptual structures learned by speech and textual models both individually and jointly. We employ Latent Concept Analysis, an unsupervised method for uncovering and interpreting latent representations in neural networks, to examine how semantic abstractions form across modalities. For reproducibility we made scripts and other resources available to the community.
FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion
Authors: Shunian Chen, Xinyuan Xie, Zheshu Chen, Liyan Zhao, Owen Lee, Zhan Su, Qilin Sun, Benyou Wang
Date: 2025-06-01 00:00:00
Tasks: Audio captioning, Caption Generation, Instruction Following, Large Language Model

High-quality, large-scale audio captioning is crucial for advancing audio understanding, yet current automated methods often generate captions that lack fine-grained detail and contextual accuracy, primarily due to their reliance on limited unimodal or superficial multimodal information. Drawing inspiration from human auditory perception, which adeptly integrates cross-modal cues and performs sophisticated auditory scene analysis, we introduce a novel two-stage automated pipeline. This pipeline first employs specialized pretrained models to extract diverse contextual cues (e.g., speech, music, general sounds, and visual information from associated video). A large language model (LLM) then synthesizes these rich, multimodal inputs to generate detailed and context-aware audio captions. Key contributions of this work include: (1) the proposed scalable method for fine-grained audio caption generation; (2) FusionAudio, a new large-scale dataset comprising 1.2 million such detailed captions, combined with 6 million QA pairs; and (3) enhanced audio models developed using FusionAudio, specifically a CLAP-based audio encoder with superior audio-text alignment and instruction following. This paper paves the way for more nuanced and accurate automated understanding of complex audio environments. Code and data can be found in https://github.com/satsuki2486441738/FusionAudio.
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition
Authors: Ziwei Gong, Pengyuan Shi, Kaan Donbekci, Lin Ai, Run Chen, David Sasu, Zehui Wu, Julia Hirschberg
Date: 2025-06-01 00:00:00
Tasks: Contrastive Learning, Emotion Recognition, Speech Emotion Recognition

Speech Emotion Recognition (SER) has seen significant progress with deep learning, yet remains challenging for Low-Resource Languages (LRLs) due to the scarcity of annotated data. In this work, we explore unsupervised learning to improve SER in low-resource settings. Specifically, we investigate contrastive learning (CL) and Bootstrap Your Own Latent (BYOL) as self-supervised approaches to enhance cross-lingual generalization. Our methods achieve notable F1 score improvements of 10.6% in Urdu, 15.2% in German, and 13.9% in Bangla, demonstrating their effectiveness in LRLs. Additionally, we analyze model behavior to provide insights on key factors influencing performance across languages, and also highlighting challenges in low-resource SER. This work provides a foundation for developing more inclusive, explainable, and robust emotion recognition systems for underrepresented languages.
PseudoVC: Improving One-shot Voice Conversion with Pseudo Paired Data
Authors: Songjun Cao, Qinghua Wu, Jie Chen, Jin Li, Long Ma
Date: 2025-06-01 00:00:00
Tasks: Voice Conversion

As parallel training data is scarce for one-shot voice conversion (VC) tasks, waveform reconstruction is typically performed by various VC systems. A typical one-shot VC system comprises a content encoder and a speaker encoder. However, two types of mismatches arise: one for the inputs to the content encoder during training and inference, and another for the inputs to the speaker encoder. To address these mismatches, we propose a novel VC training method called \textit{PseudoVC} in this paper. First, we introduce an innovative information perturbation approach named \textit{Pseudo Conversion} to tackle the first mismatch problem. This approach leverages pretrained VC models to convert the source utterance into a perturbed utterance, which is fed into the content encoder during training. Second, we propose an approach termed \textit{Speaker Sampling} to resolve the second mismatch problem, which will substitute the input to the speaker encoder by another utterance from the same speaker during training. Experimental results demonstrate that our proposed \textit{Pseudo Conversion} outperforms previous information perturbation methods, and the overall \textit{PseudoVC} method surpasses publicly available VC models. Audio examples are available.