Abstract

Traditionally, dysarthric speech intelligibility assessment systems have focused on speech as the primary input, utilizing methods such as extraction of relevant speech features, classification models, alignment of Automatic Speech Recognition (ASR) outputs, and comparisons between speech representations of dysarthric and healthy speakers. However, to achieve an automated intelligibility assessment that closely mirrors the auditory-perceptual evaluations conducted by clinicians, a model that captures both the acoustic characteristics of dysarthric speech and the linguistic structure related to word pronunciation are needed. Inspired by the practices of clinicians, this study introduces a novel text-guided dysarthric speech intelligibility assessment framework that leverages custom keyword spotting (DySIA-CKWS). The model evaluates intelligibility by detecting specific keywords and is extensively tested using UA-Speech database for speaker-wise analysis and across word groups of varying complexity. To ensure robustness, the system's performance is further validated on TORGO database, demonstrating its adaptability in cross-database settings. Statistical analysis demonstrates strong alignment between predicted and subjective intelligibility scores, with a Pearson Correlation Coefficient (PCC) of 0.9588 and a Spearman's Correlation Coefficient (SCC) of 0.9141, achieved using the proposed system on the UA-Speech database. The findings emphasize the importance of word selection and showcase the model's effectiveness in diagnosing dysarthric speech, offering a significant advancement in intelligibility assessment methodologies.

Abstract

Humans have the ability to utilize visual cues, such as lip movements and visual scenes, to enhance auditory perception, particularly in noisy environments. However, current Automatic Speech Recognition (ASR) or Audio-Visual Speech Recognition (AVSR) models often struggle in noisy scenarios. To solve this task, we propose a model that improves transcription by correlating noise sources to visual cues. Unlike works that rely on lip motion and require the speaker's visibility, we exploit broader visual information from the environment. This allows our model to naturally filter speech from noise and improve transcription, much like humans do in noisy scenarios. Our method re-purposes pretrained speech and visual encoders, linking them with multi-headed attention. This approach enables the transcription of speech and the prediction of noise labels in video inputs. We introduce a scalable pipeline to develop audio-visual datasets, where visual cues correlate to noise in the audio. We show significant improvements over existing audio-only models in noisy scenarios. Results also highlight that visual cues play a vital role in improved transcription accuracy.

Abstract

Automatic syllable stress detection traditionally relies on heuristic features like energy, duration, and pitch. Recent self-supervised models, such as wav2vec 2.0, capture stress cues more effectively, enhancing scalability. These models encode different information across layers, yet existing research focuses mainly on final-layer features, leaving intermediate layers unexplored. Stress detection is particularly challenging for non-native speakers due to deviations from correct stress patterns. This study analyzes all layers of self-supervised models to identify the most effective for stress detection. We evaluate wav2vec 2.0 (base, large, xlsr) on supervised and unsupervised classifiers using the ISLE corpus of non-native German and Italian speakers. Results show that wav2vec 2.0-large achieves the highest accuracy (96.43% for German, 96.68% for Italian) at layers 16 and 13, respectively, improving by 2.9% and 3.3% over the baseline.

Abstract

Brachiation, inspired by ape locomotion, involves swinging from one substrate to another. Existing approaches typically rely on simple grippers and computationally expensive optimal control to compute feasible states and control trajectories. In contrast, learning-based methods often lack physical modeling and require extensive training data. We present a brachiating system using high degree-of-freedom anthropomorphic hands to generate swing trajectories and perform stable grasps in a physics-based simulation environment (MuJoCo). An optimal open-loop trajectory is first generated via trajectory optimization based on a desired grasp location. A tracking controller follows this reference, while a grasping controller activates upon proximity to ensure secure contact. To reduce computational cost, we train a Generative Adversarial Imitation Learning (GAIL) policy using expert trajectories from the optimization framework. The GAIL-based controller generalizes to perturbed conditions and eliminates the need for repeated re-optimization, significantly lowering computation time. It also adapts to varying initial configurations, removing the requirement to rerun optimization for each case. We compare the learned model with a traditional optimal controller and demonstrate marked improvements in both computational efficiency and versatility.

Abstract

Background Heggli et al. (2021) noted that circadian rhythms and individual traits influence diurnal patterns in music preference. Depressive individuals often experience more negative moods in the evening, known as the evening-worsening effect (Rusting & Larsen, 1998). As music is often used for emotion focused-coping during such mood states (Stewart et al., 2019), depressive individuals’ music consumption in the evening are unclear. Aims This study explores the evening-worsening effect in music listening, examining if depressive individuals prefer low-valence, low-arousal music in the evening. Methods A survey of 290 Indians (M=20.35yrs, SD=2.08, 211 males) collected Kessler Psychological Distress scores (K10) and Healthy-Unhealthy music scale (HUMS) scores along with Spotify listening histories over one year. Additional measures like trait empathy and life satisfaction were collected but not analysed. Participants were classified as No-Risk (K10<20, n=114, 82 males) or At-Risk (K10≥29, n=81, 52 males) of depression (Andrews & Slade, 2001). Spotify valence and energy features for top 100-250 songs over the past 4-8 weeks from the time they filled in the survey were analyzed alongside timestamps. K-Means clustering divided the day into early hours, morning, afternoon and evening, and group differences in hourly valence and energy averages were assessed using two-way ANOVA and t-tests. Results At-Risk had significantly higher Unhealthy scores than No-Risk (t=8.15, p<0.001). Significant differences in valence and energy were found between At-Risk and No-Risk only in the afternoon (valence: t=-9.47, energy: t=-6.53) and evening (valence: t=-2.98, energy: t=-11.56)(all p≤ 0.05). Two-way ANOVA showed significant main effects of time, group, and their interaction for valence (time: F(3, 40)=22.86; group: F(1, 40)=12.36; interaction: F(3, 40)=6.65) and energy (time: F(3, 40)=23.22; group: F(1, 40)=98.73; interaction: F(3, 40)=13.57)(all p<0.001), consistent across the top 100-250 songs and the past 4-8 weeks of listening histories. Conclusions This study confirms diurnal patterns in music preferences, with At-Risk individuals favouring low-valence and low-energy music, especially in the afternoon and evening. These results align with Rusting and Larsen’s (1998) evening-worsening effect, linking depressive tendencies to heightened negative moods in the evening. The findings suggest potential maladaptive listening behaviors, needing further research.

Abstract

Most research on the presence and activities of Muslim intellectuals in interwar Europe deals with men, with Muslim women strikingly underrepresented in such scholarship. This article addresses this gap by dwelling on the activities of Haidarābādī reformer and writer Ṣuġhrā Humāyūñ Mirzā (1884–1958) in Berlin in 1924 and, in the process, offering a glimpse into the role and work of women activists, educators, social workers, and intellectuals working in Germany at the time. Her own thought in this regard makes for an important and fascinating study. Furthermore, the speeches she was invited to deliver by different associations contain striking historical commentary and communicate her thoughts and experiences as a social reformer and educator in Europe. In examining this account, this paper also demonstrates how Ṣuġhrā Begam’s insatiable personal curiosity and relentless sense of duty critically challenge and undermine the enforced, unhistorical invisibility of Muslim women travellers in the hegemonic Eurocentric discourse of travel writing.

Abstract

We develop randomized quantum algorithms to simulate quantum collision models, also known as repeated interaction schemes, which provide a rich framework to model various open-system dynamics. The underlying technique involves composing time evolutions of the total (system, bath, and interaction) Hamiltonian and intermittent tracing out the environment degrees of freedom. This results in a unified framework where any near-term Hamiltonian simulation algorithm can be incorporated to implement an arbitrary number of such collisions on early fault-tolerant quantum computers: we do not assume access to specialized oracles such as block encodings and minimize the number of ancilla qubits needed. In particular, using the correspondence between Lindbladian evolution and completely positive trace-preserving maps arising out of memoryless collisions, we provide an end-to-end quantum algorithm for simulating Lindbladian dynamics. For a system of 𝑛-qubits, we exhaustively compare the circuit depth needed to estimate the expectation value of an observable with respect to the reduced state of the system after time 𝑡 while employing different near-term Hamiltonian simulation techniques, requiring at most 𝑛+2 qubits in all. We compare the cnot gate counts of the various approaches for estimating the transverse field magnetization of a 10-qubit XX-Heisenberg spin chain under amplitude damping. Finally, we also develop a framework to efficiently simulate an arbitrary number of memory-retaining collisions, i.e., where environments interact, leading to non-Markovian dynamics. Overall, our methods can leverage quantum collision models for both Markovian and non-Markovian dynamics on early fault-tolerant quantum computers, shedding light on the advantages and limitations of simulating open systems dynamics using this framework.

Abstract

Stable aerial manipulation during dynamic tasks such as object catching, perching, or contact with rigid surfaces necessarily requires compliant behavior, which is often achieved via impedance control. Successful manipulation depends on how effectively the impedance control can tackle the unavoidable coupling forces between the aerial vehicle and the manipulator. However, the existing impedance controllers for aerial manipulator either ignore these coupling forces (in partitioned system compliance methods) or require their precise knowledge (in complete system compliance methods). Unfortunately, such forces are very difficult to model, if at all possible. To solve this long-standing control challenge, we introduce an impedance controller for aerial manipulator which does not rely on a priori knowledge of the system dynamics and of the coupling forces. The impedance control design can address unknown coupling forces, along with system parametric uncertainties, via suitably designed adaptive laws. The closed-loop system stability is proved analytically and experimental results with a payload-catching scenario demonstrate significant improvements in overall stability and tracking over the state-of-the-art impedance controllers using either partitioned or complete system compliance.

Abstract

Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlleD experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM- generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM’s task-solvinG processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities.

Abstract

Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely solely on graph structure, overlooking the rich semantic signals encoded in textual attributes. We introduce SEMMA, a dual-module KGFM that systematically integrates transferable textual semantics alongside structure. SEMMA leverages Large Language Models (LLMs) to enrich relation identifiers, generating semantic embeddings that subsequently form a textual relation graph, which is fused with the structural component. Across 54 diverse KGs, SEMMA outperforms purely structural baselines like ULTRA in fully inductive link prediction. Crucially, we show that in more challenging generalization settings, where the test-time relation vocabulary is entirely unseen, structural methods collapse while SEMMA is 2x more effective. Our findings demonstrate that textual semantics are critical for generalization in settings where structure alone fails, highlighting the need for foundation models that unify structural and linguistic signals in knowledge reasoning.