arXiv Machine Learning

Trajectory Dynamics in Self-Supervised Learning Latent Space for Audio Deepfake Detection

arXiv:2608. 13817v1 Announce Type: cross Abstract: Human speech production is constrained by physiology, giving rise to characteristic temporal structure on acoustic signals.

arXiv AI

Aug 21

Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners

arXiv:2608. 19863v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes to reach competitive performance.

By Umberto Cappellazzo, Xubo Liu, Stavros Petridis, Maja Pantic

llms rag fine-tuning benchmarks

arXiv AI

Jun 19

FlowFake: Liquid Networks for Audio Deepfake Detection

arXiv:2606. 19579v1 Announce Type: cross Abstract: Audio deepfakes generated by neural text-to-speech and voice-cloning systems threaten speaker verification and public discourse at scale.

By Shivaay Dhondiyal, Divyansh Sharma, Dinesh Kumar Vishwakarma

multimodal benchmarks

arXiv AI

Sep 1

TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models

TEMPO is a unified model that adds temporally‑grounded capabilities to large audio‑language models, enabling timestamping of events, speakers, and sounds in audio, speech, and music. It introduces a supervised fine‑tuning stage featuring atomic timestamp tokens, a time‑aware projector with sinusoidal encodings, and a distance‑aware Gaussian loss, trained via a synthetic‑to‑real curriculum. Additionally, TEMPO employs reinforcement learning (GRPO) as a refinement step, and achieves state‑of‑the‑art performance on a benchmark of 10K samples across five timestamping tasks, surpassing Audio Flamingo Next and Qwen3‑Omni.

By Apoorva Kulkarni, Kaousheik Jayakumar, Sreyan Ghosh, Utathya Aich, Ramani Duraiswami, Dinesh Manocha

llms rag reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Aug 21

Towards Quantifying Benchmark Optimization in ASR Models

arXiv:2608. 19936v1 Announce Type: cross Abstract: Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities.

By Theo Lebryk, David Ayllon, Alice Baird, Jakub Piotr C{\l}apa, Jens Madsen, Panagiotis Tzirakis

multimodal benchmarks

arXiv AI

Jun 30

How to Leverage Synthetic Speech for LLM-Based ASR Systems?

arXiv:2606. 29031v1 Announce Type: cross Abstract: In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is an appealing alternative for training automatic speech recognition (ASR) without exposing sensitive customer recordings.

By Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso, S\'everin Baroudi, Shashi Kumar, Esa\'u Villatoro-Tello, Srikanth Madikeri, Manjunath K E, Old\v{r}ich Plchot, Kadri Hacio\u{g}lu, Petr Motlicek, Andreas Stolcke

llms multimodal safety

arXiv AI

Jun 8

dots.tts Technical Report

arXiv:2606. 07080v1 Announce Type: cross Abstract: We present dots.

By Shi Lian, Changtao Li, Bohan Li, Hankun Wang, Da Zheng, Junfeng Tian, Yufeng Ma, Colin Zhang, Kai Yu

diffusion efficiency multimodal benchmarks

arXiv AI

Jul 1

BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations

arXiv:2606. 30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks.

By Ludovic K. Tuncay (IRIT-SAMoVA), Etienne Labb\'e (IRIT-SAMoVA), Thomas Pellegrini (IRIT-SAMoVA)

llms benchmarks

arXiv AI

Sep 15

Modeling, Scaling, and Decoding: Optimizing Controllable Speech Generation with Nonverbal Vocalizations

arXiv:2609.14231v1 Announce Type: cross Abstract: Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their aco...

By Ziyu Zhang, Yun Chen, Taihui Wang, Hanzhao Li, Qicong Xie, Rilin Chen, Zhixian Zhao, Lei Xie

fine-tuning

arXiv AI

Jun 18

Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

arXiv:2606. 19325v1 Announce Type: cross Abstract: Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings.

By Michael Finkelson, Daniel Segal, Eitan Richardson, Shahar Armon, Nani Goldring, Poriya Panet, Nir Zabari, Benjamin Brazowski, Or Patashnik, Yoav HaCohen

rag benchmarks

arXiv AI

Jun 10

Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation

arXiv:2606. 10368v1 Announce Type: cross Abstract: Speech-to-text (S2T) systems for recognition (ASR) and translation (S2TT) typically generate discrete text tokens.

By Xuanchen Li, Tianrui Wang, Yuheng Lu, Zikang Huang, Yu Jiang, Chenghan Lin, Chenrui Cui, Ziyang Ma, Xingyu Ma, Chunyu Qiang, Guochen Yu, Xie Chen, Longbiao Wang, Jianwu Dang

diffusion multimodal

arXiv AI

Jun 11

Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering

arXiv:2606. 11386v1 Announce Type: cross Abstract: Full-duplex spoken language models (FD-SLMs) enable seamless speech interaction by allowing models to listen and speak simultaneously, yet the internal mechanism by which they coordinate listening and speaking remains underexplored.

By Cheng-Kuang Chang, Kai-Wei Chang, Alexander H. Liu, James Glass

llms fine-tuning benchmarks

arXiv AI

Aug 11

VoxZip: Semantic-Anchored Temporal KV Cache Compression for Long-Context Audio Inference

arXiv:2608. 08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks.

By Wenxu Jia, Dongjie Fu, Xize Cheng, Fangming Feng, Linjun Li, Wenshi Chen, Yingming Li, Zhou Zhao, Tao Jin

llms efficiency multimodal benchmarks safety

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