arXiv Machine Learning August 17, 2026 By Tom\'as Andrade Weber

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

Read the original on arXiv Machine Learning →

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

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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

One email a morning, machine-written

Related stories

Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners

FlowFake: Liquid Networks for Audio Deepfake Detection

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

Towards Quantifying Benchmark Optimization in ASR Models

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

dots.tts Technical Report