The paper presents a new wake‑up system for voice assistants that goes beyond simple keyword spotting by adding contextual trigger detection. After the wake word is heard, the system reasons to differentiate between actual user commands and unrelated speech, enabling more efficient and context‑aware interactions. A data‑generation architecture is introduced that creates a 62.3‑hour corpus of controllable multi‑speaker conversations, including direct invocations, contextual follow‑ups, and non‑addressed speech, and experimental results confirm the approach’s effectiveness across varied synthetic scenarios.
By Marcin Sowa\'nski, Kacper Leszczy\'nski, Kacper Krzywicki, Krzysztof Wodnicki
arXiv:2607. 16736v1 Announce Type: cross Abstract: This paper presents RealDESED, a real-world domestic sound event detection (SED) benchmark comprising 5,710 audio recordings collected by 652 participants in their homes.
By Florian Schmid, Paul Primus, Alexander Fichtinger, Tara Jadidi, Tobias Morocutti, Gerhard Widmer
arXiv:2610.16049v1 Announce Type: cross
Abstract: Existing audio deepfake detection (ADD) methods frequently exhibit limited generalization, as fixed bottleneck architectures fail to adapt to the het...
By Jianqiao Cui, Haoxian Zhu, Hanbiao Meng, Yanghao Zhang
arXiv:2607. 28351v2 Announce Type: replace-cross Abstract: Speech deepfake detection has expanded in scope with increasingly heterogeneous spoofing mechanisms, including speech synthesis, voice conversion, vocoder reconstruction, and neural-codec resynthesis.
By Wan Lin, Li Wang, Jindong Wang, Kunyu Feng, Zhizheng Wu
arXiv:2606. 30780v1 Announce Type: cross Abstract: Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers.
By Octavian Pascu, Dan Oneata, Horia Cucu, Nicolas M. Muller
arXiv:2609.26648v1 Announce Type: cross
Abstract: Active speaker detection (ASD) requires reliable association between visible faces and acoustic speech, yet existing systems often degrade under chal...
By Pu Wang, Yujun Wang, Hugo Van hamme
arXiv:2506.08911v2 Announce Type: replace-cross
Abstract: This paper presents a keyword spotting (KWS) system implemented on the NXP MCXN947 microcontroller with an integrated Neural Processing Unit...
By Petar Jaku\v{s}, Hrvoje D\v{z}apo
Samsone is a family of small audio language models (SALMs) designed for on‑device inference, with the flagship Samsone‑134M setting a new state‑of‑the‑art for its size class across multiple benchmarks. The paper also presents Samsone‑99M and Samsone‑356M to study scaling laws, showing that these compact models achieve performance competitive with much larger counterparts. The authors train the models on publicly available data and release training code, weights, mobile‑optimized checkpoints, and an open‑source Android app for real‑time inference.
By Piotr Masztalski, Micha{\l} K. Grzeszczyk, Olaf Sikorski
arXiv:2606. 04205v1 Announce Type: cross Abstract: The growing popularity and capacity of generative models have eroded the distinction between human and machine-generated content, motivating a growing body of work on detection across text, images, and audio.
By Sajad Ebrahimi, Nima Jamali, Bardia Shirsalimian, Kelly McConvey, Wentao Zhang, Jalehsadat Mahdavimoghaddam, Maksym Taranukhin, Maura Grossman, Vered Shwartz, Yuntian Deng, Ebrahim Bagheri
arXiv:2611.06496v1 Announce Type: new
Abstract: Most speech large language models (SLMs) still process speech in an offline manner. They only start responding after they receive the complete speech s...
By Zewen Zhan, Hui Lu, Xueyuan Chen, Huimeng Wang, Yikai Huang, Xixin Wu, Shiyin Kang, Bin Xia, Zhiyong Wu
arXiv:2510. 12947v3 Announce Type: replace-cross Abstract: Voice activity detection (VAD) serves as an early gate in voice-assistant pipelines for smart devices.
By Hamed Jafarzadeh Asl, Amin Edraki, Mahsa Ghazvini Nejad, Masoud Asgharian, Mohammadreza Sadeghi, Yuanhao Yu, Vahid Partovi Nia
The paper introduces a new target‑speaker unlearning task for automatic speech recognition (TSU‑ASR) that allows certain speakers to opt out of transcription while still indicating their presence. A lightweight Enrollment‑Conditioned Gating (ECG) module is added to a frozen dual‑stream speech LLM, enabling dynamic unlearning of new opt‑out speakers during inference. Experiments on AMI and AliMeeting datasets show significant drops in transcription accuracy for opt‑out speakers while preserving performance for retained speakers.
By Bo Su, Yueru Yan, Thai Le