124 papers
This paper introduces PolyBench, a new benchmark designed to evaluate compositional reasoning in polyphonic audio across five distinct tasks, revealing that current Large Audio Language Models consistently struggle with the complexity of concurrent sound events.
This paper investigates model merging as a scalable alternative to full fine-tuning for multi-domain ASR, benchmarking 11 algorithms across 10 European Portuguese domains and introducing a novel "BoostedTSV-M" method that outperforms full fine-tuning while preserving out-of-distribution generalization.
This paper proposes Visual-Informed Neural Beamforming Network (VI-NBFNet), an end-to-end audiovisual framework that integrates lip movement features extracted from a pretrained visual model with microphone array signals via an attention mechanism to significantly enhance speech quality and robustness in challenging, dynamic, and low-SNR environments.
This paper presents a novel framework for the simultaneous acquisition of real-time MRI, EEG, and surface EMG to capture brain, muscle, and articulatory activity during speech, featuring a specialized artifact suppression pipeline to overcome technical challenges and enable unprecedented insights into speech neuroscience.
This paper addresses the underexplored role of temporal pooling in training-free anomalous sound detection by proposing and evaluating adaptive strategies, specifically Relative Deviation Pooling (RDP) and a hybrid approach, which achieve state-of-the-art performance across multiple benchmarks and outperform previously reported trained systems.
This paper introduces VoxKnesset, a large-scale open-access dataset of 2,300 hours of longitudinal Hebrew parliamentary speech spanning 2009–2025, which is used to benchmark and demonstrate the challenges of speaker verification and age prediction over time, revealing significant performance degradation in standard models as speakers age.
This paper introduces Aurchestra, a novel system for resource-constrained hearables that enables real-time, fine-grained control over up to five overlapping sound sources by combining a dynamic interface with an optimized on-device multi-output extraction network, effectively transforming the acoustic environment into a programmable mix.
This paper introduces RA-QA, a comprehensive benchmarking system featuring a standardized pipeline, a large-scale dataset of 9 million diverse question-answer pairs, and a unified evaluation protocol to assess and expose the limitations of respiratory audio question-answering models under real-world heterogeneity.
This paper proposes a multi-loss learning framework for speech emotion recognition that integrates energy-adaptive mixup and frame-level attention to address data scarcity and emotional complexity, achieving state-of-the-art performance across four benchmark datasets.
The paper introduces Schrödinger Bridge Mamba (SBM), a novel one-step speech enhancement model that synergizes the Schrödinger Bridge training paradigm with the Mamba architecture to achieve superior denoising and dereverberation performance with real-time feasibility, outperforming strong generative and discriminative baselines.
This paper introduces Noise-to-Notes (N2N), a state-of-the-art diffusion-based framework that redefines automatic drum transcription as a conditional generative task, utilizing an Annealed Pseudo-Huber loss for joint optimization and music foundation model features to achieve superior robustness and performance across multiple benchmarks.
This paper reveals that SpeechLLM backbones struggle with conversational disfluencies due to a bias toward semantic abstraction over structural fidelity, with performance varying by architecture and fine-tuning often compromising generalization despite achieving state-of-the-art results.
The paper introduces SAM, a State-space Audio-language Model leveraging a Mamba-2 backbone that achieves competitive performance with fewer parameters than larger transformer models while establishing key design principles regarding joint encoder finetuning, optimal token representation, and instruction-following supervision.
The paper introduces BabyHuBERT, a multilingual self-supervised speech model trained on 13,000 hours of child-centered recordings that significantly outperforms existing adult-focused models in segmenting speakers within diverse, naturalistic child language datasets.
This paper proposes TSPC, a novel two-stage phoneme-centric architecture that leverages an extended Vietnamese phoneme set as an intermediate representation to significantly improve Vietnamese-English code-switching speech recognition accuracy while maintaining computational efficiency.
This study conducts a large-scale probing analysis of 11 self-supervised speech models to reveal a hierarchical encoding of speaker attributes, challenging the assumption that final layers are purely linguistic by showing that larger models recover speaker identity in deep layers while intermediate representations better capture dynamic prosody than specialized embeddings.
This paper presents the CP-JKU team's two-stage system for the ICASSP 2025 Music Source Restoration Challenge, which combines a curriculum-trained BandSplit-RoFormer model for separating eight stems and a specialized HiFi++ GAN for restoring instrument-specific waveforms from mastered audio.
This paper introduces SMMA, a fully automated deep learning framework that accurately measures geniohyoid muscle thickness during speech, enabling scalable analysis of speech motor control and objective assessment of related disorders by eliminating the need for time-consuming manual annotation.
This paper introduces a probabilistic early-exit framework for single-channel speech separation and enhancement that dynamically scales computational resources based on uncertainty-aware signal quality estimates, enabling efficient deployment on heterogeneous devices without compromising reconstruction performance.
The paper proposes ZeSTA, a domain-conditioned training framework that effectively leverages zero-shot TTS synthetic data for low-resource personalized speech synthesis by distinguishing real and synthetic inputs via lightweight embeddings and real-data oversampling, thereby improving speaker similarity without compromising quality.