Applied & Human-Centered · Computer Science & AI
Sound
The newest papers in Sound that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
553 papers, from 4 September 2026 to 5 November 2026: 553 arXiv preprints.
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Newest papers, page 7
DF-PALM: Dynamic Fusion Prompt Learning for Audio--Language Models
Few-shot audio classification requires audio--language models to distinguish variable acoustic events from limited labeled data.Shared-Private Cross-Modal Discrepancy Learning for Imbalanced Multi-Source Audio-Visual Deepfake Detection
Audio-visual deepfake detectors are often trained under a single dataset or manipulation regime, but multi-source training collections may contain heterogeneous attacks and uneven source…CASE: Complementary Articulation-Aware Speech Encoder to Enhance Speech-Driven 3D Facial Animation
General-purpose self-supervised speech encoders provide strong acoustic representations, but their pretraining objectives are not explicitly aligned with the visible articulatory dynamics required…Whistle and Wrong Lyrics: Synthetic Queries Are Not Enough for Real-World Query-by-Melody
People look up a song by humming it, but also by whistling it or singing it with the wrong words.How Far Can the Gemma 4 Speech Encoder Go Without Changing Its Weights? Saudi and Gulf Arabic Speech Recognition with a Frozen and an Adapter-Augmented Encoder
We study the 305M-parameter audio encoder shared by Gemma 4 and EmbeddingGemma 2 (identical except for its final projection) for Saudi and Gulf Arabic speech recognition, without changing any of its…Long Listening Thoughts: Eliciting Open Auditory Reasoning with Deliberative Perception and Cognitive Refinement
We introduce Long Listening Thoughts (LLT), a framework for constructing post-training datasets that improves audio reasoning by pairing deliberate perceptual grounding with explicit cognitive…SENTRY: A Streaming Edge Network Trust Engine for Real-Time Audio Deepfake Detection
Real-time voice cloning has enabled large-scale streaming deepfake and vishing attacks, while existing Network Detection and Response (NDR) solutions rely on GPU-based post-processing that requires…MOMO: Human Motion from Moving Sounds
Human responses to sound depend not only on its semantic content, but also on the evolving spatial relationship between source and listener, conveyed through spatial audio and its temporal dynamics.ASR Personalization in Pathological Voice: Accuracy, Efficiency, and Duration Effects
Automatic Speech recognition (ASR) struggles with Pathological speech due to persistent speaker-specific mismatch.UniAVC: Unified Audio-Video Creation via Progressive Training and Asymmetric Scheduling
Unified audio-video creation requires a single model capable of text-conditioned joint synthesis, bidirectional cross-modal generation, and instruction-guided editing across both modalities.Dual Task Vector Merging: Leveraging Emotion and ASR Knowledge for Low-Resource Alzheimer's Disease Detection
Alzheimer's Disease (AD) detection from speech benefits from both paralinguistic and linguistic cues, yet model training is frequently constrained by limited data.ShlokaDhara: A Multi-Speaker Sanskrit Speech Corpus for Learning Meter-Aware Prosody in Text-to-Recitation
Sanskrit verse recitation is governed by $\textit{Chandas}$, a strict prosodic framework defined by rhythmic $\textit{Laghu}$ (light) and $\textit{Guru}$ (heavy) syllables.Too Emotional to Be Fake? Intra-Utterance Emotion Shifts Expose Failure Modes in Speech Deepfake Detectors
Expressive speech synthesis can now change emotion within a single utterance, yet its impact on deepfake detection remains unclear.Semantic-Critical Regions for Video-to-Audio Generation
In real-world videos, sound events are often associated with localized and fine-grained visual cues, such as object motion, object-part dynamics, contact events, and interactions between objects.Framing Spatial Hearing within the Free Energy Principle: A Hierarchical Model of Perceptual Learning
Spatial hearing relies on dynamic internal representations to decode spatial information from ever-changing acoustic conditions.Flash RNN-T: Memory-Efficient Joint Network and Transducer Loss Computation
The recurrent neural network transducer (RNN-T) remains a practical choice for streaming speech recognition, but training requires large intermediate tensors.Interpreting SSL Representations for Spoof Detection: a WavLM Study
Self-supervised speech models such as WavLM achieve strong spoofing detection performance, yet which layers encode the most spoof-relevant information and what acoustic properties they capture…ConvexLLM: Scaling Convex-Hull Speech Interfaces to General-Purpose Audio Intelligence
Speech and audio language models connect continuous acoustic representations to decoders pretrained primarily on text data.Beyond the Benchmark: Conditioning, Domains and Failure Modes of Qwen3-TTS
Qwen3-TTS reports state-of-the-art zero-shot voice cloning, but its technical report leaves open how the model should be conditioned, how it behaves outside read speech, and how it fails.VocalGrad: Evaluating Acoustic Perception in Audio Language Models
Recent large audio language models (LALMs) have extended their capabilities beyond lexical speech understanding to tasks requiring paralinguistic and non-linguistic audio understanding, such as…