arXiv:2510. 25960v2 Announce Type: replace-cross Abstract: In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands.
arXiv:2608. 15636v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment.
arXiv:2605. 26310v2 Announce Type: replace Abstract: The detection of unmanned aerial vehicles (UAVs) is important for the protection of civilian and military infrastructure.
The paper introduces RALCT, a lightweight Convolutional Transformer that combines randomized audio augmentations, MFCCs, and log‑mel spectrograms to extract robust features for environmental sound recognition. With only about 310,000 parameters, RALCT achieves state‑of‑the‑art accuracy—over 93% on UrbanSound8K, peaking at 94.56%—making it suitable for deployment on mobile devices. The authors also develop a mobile app that integrates the model to provide real‑time safety alerts for hearing‑impaired users.
The paper presents a compact underwater acoustic classification framework that integrates multi-representation feature engineering, temporal statistical pooling, and lightweight convolutional architectures for acoustic time-frequency and cochlear representations. Experiments on the ShipsEar dataset show a two-layer CNN achieving a macro F1 of 0.9918 and an RBF-SVM reaching 0.9883, but recording provenance issues limit verification of generalisation. When evaluated on the DeepShip dataset with recording-level partitioning, a 157K-parameter CNN attains a macro F1 of 0.7226, while a larger ResNet18 does not improve validation performance, underscoring the need for representation-aware design and rigorous evaluation for deployable systems.
ECHO-G is a framework for generating full‑body co‑speech motion for humanoid robots, jointly conditioned on speech audio and timed transcripts. Its Speech‑Grounded Diffusion Transformer (SGDiT) fuses frame‑aligned acoustic features with token‑level linguistic context, preserving distinct granularities while modeling one‑to‑many utterance‑motion relationships directly in robot space. The authors introduce a BEAT2‑derived audio‑text‑robot dataset, a benchmark for co‑speech characteristics, robot‑motion quality, and runtime efficiency, and demonstrate that direct robot‑space generation outperforms human‑motion generation and retargeting pipelines, with joint audio‑text conditioning yielding superior results in both quantitative evaluation and a video‑rating study. "whyItMatters":"The study provides a new dataset, benchmark, and a demonstrably effective method for generating realistic co‑speech motion directly in robot space, advancing practical humanoid robot interaction."
arXiv:2508. 12435v2 Announce Type: replace-cross Abstract: While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors.
arXiv:2508. 02425v2 Announce Type: replace-cross Abstract: In physical human-robot collaboration (pHRC) settings, humans and robots collaborate directly in shared environments.
NAVIR is an end‑to‑end audio‑visual speech recognition system designed for the BrainChip Akida neuromorphic processor, which only supports sequential 2‑D convolutions. The architecture separates spatial and temporal encoding into three AkidaNet modules—per‑frame visual, temporal video, and spectrogram audio encoders—fused by a lightweight predictor and decoded with constrained beam search. Trained with CTC on noise‑augmented audio and fine‑tuned via quantization‑aware training, the quantized model achieves 14.0% WER on GRID’s unseen‑speaker split and 3.3% on overlapped‑speaker split, outperforming audio‑only baselines, and delivers 98.6% command accuracy at 1.5% WER on an industrial‑command corpus, while offering a 13‑fold energy advantage over conventional ANNs and roughly 5‑fold lower energy per inference than a Raspberry Pi CPU.
arXiv:2606. 08247v1 Announce Type: cross Abstract: Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing, mental status, and response to reliever therapy.
arXiv:2606. 18122v1 Announce Type: cross Abstract: Embedded machine learning moves inference from cloud services to resource-constrained devices that must acquire data, preprocess signals, run a model, and act within tight limits on memory, energy, and latency.
arXiv:2606. 06837v1 Announce Type: cross Abstract: Scripted vs spontaneous speech detection is appealing for interview guardrails, but benchmark performance can be inflated by shortcuts tied to corpus identity, channel conditions, and recording artifacts rather than speaking style itself.
arXiv:2606. 14658v1 Announce Type: cross Abstract: Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras.