Abstract

We introduce StethoSpeech, a silent speech interface that transforms flesh-conducted vibrations behind the ear into speech. This innovation is designed to improve social interactions for those with voice disorders, and furthermore enable discreet public communication. Unlike prior efforts, StethoSpeech does not require (a) paired-speech data for recorded vibrations and (b) a specialized device for recording vibrations, as it can work with an off-the-shelf clinical stethoscope. The novelty of our framework lies in the overall design, simulation of the ground-truth speech, and a sequence-to-sequence translation network, which works in the latent space. We present comprehensive experiments on the existing CSTR NAM TIMIT Plus corpus and our proposed StethoText: a large-scale synchronized database of non-audible murmur and text for speech research. Our results show that StethoSpeech provides natural-sounding and intelligible speech, significantly outperforming existing methods on several quantitative and qualitative metrics. Additionally, we showcase its capacity to extend its application to speakers not encountered during training and its effectiveness in challenging, noisy environments. Speech samples are available at https://stethospeech.github.io/StethoSpeech/.

Abstract

We propose a novel approach to significantly improve the intelligibility in the Non-Audible Murmur (NAM)-to-speech conversion task, leveraging self-supervision and sequence-tosequence (Seq2Seq) learning techniques. Unlike conventional methods that explicitly record ground-truth speech, our methodology relies on self-supervision and speech-to-speech synthesis to simulate ground-truth speech. Despite utilizing simulated speech, our method surpasses the current state-of-the-art (SOTA) by 29.08% improvement in the Mel-Cepstral Distortion (MCD) metric. Additionally, we present error rates and demonstrate our model’s proficiency to synthesize speech in novel voices of interest. Moreover, we present a methodology for augmenting the existing CSTR NAM TIMIT Plus corpus, setting a benchmark with a Word Error Rate (WER) of 42.57% to gauge the intelligibility of the synthesized speech. Speech samples can be found at https://nam2speech.github.io/NAM2Speech/

Abstract

Autism Spectrum Disorder (ASD) presents challenges in communication, social interaction, and sensory processing. This pilot study introduces a GOAL driven Recommendation Framework for personalized music interventions, leveraging music's therapeutic functions to address emotional, cognitive, and social needs in individuals with ASD was tested on an Indian population. Through participants music playlist compilation and data collected from online sources, a diverse dataset is curated. The recommendation system employs similarity metric to recommend music aligned with specific therapeutic goals. Evaluation metrics and behavioural changes indicate significant improvements in mood management, sleep regularization, focus, and sensory regulation. However, enhancements in communication and social skills are less conclusive. This study underscores the feasibility and effectiveness of personalized music recommendations in addressing therapeutic goals for individuals with ASD, emphasizing the need for further research to explore longitudinal outcomes to assess its impact on the quality of life of individuals with ASD.

Abstract

Existing datasets for tabular question answering typically focus exclusively on text within cells. However, real-world data is inherently multimodal, often blending images such as symbols, faces, icons, patterns, and charts with textual content in tables. With the evolution of AI models capable of multimodal reasoning, it is pertinent to assess their efficacy in handling such structured data. This study investigates whether current AI models can perform knowledge-aware reasoning on multimodal structured data. We explore their ability to reason on tables that integrate both images and text, introducing MMTabQA, a new dataset designed for this purpose. Our experiments highlight substantial challenges for current AI models in effectively integrating and interpreting multiple text and image inputs, understanding visual context, and comparing visual content across images. These findings establish our dataset as a robust benchmark for advancing AI's comprehension and capabilities in analyzing multimodal structured data.

Abstract

A system to visualise large graphs

Abstract

The focus of recent research has shifted from merely improving the metrics based performance of Deep Neural Networks (DNNs) to DNNs which are more interpretable to humans. The field of eXplainable Artificial Intelligence (XAI) has observed various techniques, including saliency-based and concept-based approaches. These approaches explain the model's decisions in simple human understandable terms called Concepts. Concepts are known to be the thinking ground of humans}. Explanations in terms of concepts enable detecting spurious correlations, inherent biases, or clever-hans. With the advent of concept-based explanations, a range of concept representation methods and automatic concept discovery algorithms have been introduced. Some recent works also use concepts for model improvement in terms of interpretability and generalization. We provide a systematic review and taxonomy of various concept representations and their discovery algorithms in DNNs, specifically in vision. We also provide details on concept-based model improvement literature marking the first comprehensive survey of these methods.

Abstract

Robustness towards adversarial attacks is a vital property for classifiers in several applications such as autonomous driving, medical diagnosis, etc. Also, in such scenarios, where the cost of misclassification is very high, knowing when to abstain from prediction becomes crucial. A natural question is which surrogates can be used to ensure learning in scenarios where the input points are adversarially perturbed and the classifier can abstain from prediction? This paper aims to characterize and design surrogates calibrated in enquote{Adversarial Robust Reject Option} setting. First, we propose an adversarial robust reject option loss $ell_{d}^{gamma}$ and analyze it for the hypothesis set of linear classifiers ($mathcal{H}_{textrm{lin}}$). Next, we provide a complete characterization result for any surrogate to be $(ell_{d}^{gamma},mathcal{H}_{textrm{lin}})$- calibrated. To demonstrate the difficulty in designing surrogates to $ell_{d}^{gamma}$, we show negative calibration results for convex surrogates and quasi-concave conditional risk cases (these gave positive calibration in adversarial setting without reject option). We also empirically argue that Shifted Double Ramp Loss (DRL) and Shifted Double Sigmoid Loss (DSL) satisfy the calibration conditions. Finally, we demonstrate the robustness of shifted DRL and shifted DSL against adversarial perturbations on a synthetically generated dataset.

Abstract

Purpose : This study aims to develop an artificial intelligence (AI)-based system for accurately grading arrested retinal development using optical coherence tomography (OCT) across various manufacturers. We employ deep learning techniques, specifically Unsupervised Domain Adaptation (UDA), to create a device-agnostic classification model distinguishing normal and abnormal retinal development. Methods : Foveal scans from three OCT manufacturers (TM-OCT1, HH-OCT1, TM-OCT2, TM-OCT3) were collected and annotated from datasets exceeding 20,000 OCT scans. The dataset was divided into training (80%) and testing (20%) sets. We utilised Convolutional Neural Networks (CNN) with ResNet50 backbone, assessing each pair as the source (for supervised training) and target (for unsupervised training). The diagnostic accuracy of the AI models was compared to seven clinician graders with varying experience (1 to 10 years), evaluating sensitivity, specificity, and overall accuracy. Results : The cross-domain binary classification demonstrated exceptional diagnostic accuracy ranging from 87.75% to 96.06%. Sensitivity and specificity metrics further validated the robustness of our AI system (sensitivity: 88.68% to 98.38%, specificity: 78.00% to 98.02%). Notably, the model trained on TM-OCT1 and HH-OCT1 achieved 96.06% accuracy, 98.38% sensitivity, and 89.11% specificity on the HH-OCT1 test set, with an Area Under the Curve (AUC) of 99.4 (95% CI). These outcomes highlight the system's ability to distinguish normal and abnormal retinal development across diverse OCT devices. Conclusions : Our study demonstrates, for the first time, the feasibility of employing a device-agnostic AI system in paediatric OCT interpretation without additional labelled data. The AI system's diagnostic performance is comparable to a clinician with over 10 years of experience, showcasing its potential to reduce inter-examiner variability and enhance clinical care pathways. With integration of paediatric OCT into routine clinical assessment, our AI system serves as a robust foundation for the development of a real-time, frontline diagnostic tool for retinal developmental disorders.

Abstract

Flapping-wing robots (FWR) have domain-specific applications, where the lack of a fast-rotating propeller makes them safer when operating in complex environments with human proximity. However, most existing research in flapping-wing robots focuses on improving range/endurance or increasing payload capacity. This paper proposes a modular powertrain-based flapping-wing robot as a versatile solution to a mission-specific priority switch between payload or range for the same FWR. As the flapping frequency and stroke amplitude directly influence the flight characteristics of the FWR, we exploit this relation when designing our swappable powertrain with different motorgearbox combinations and 4-bar crank lengths to obtain the desired frequency and amplitude. We calculate initial estimates for default configuration and simulate it using pterasoftware. We then fabricate two powertrain modules - a default configuration with a higher flapping frequency for payload purposes and an extended-range configuration with a tandem propeller for higher flight velocity for longer range and endurance. To verify the results, we compare the flight test data of both power train configurations using the same FWR platform.

Abstract

We propose an approach to train a fair cost-based abstain option classifier. Existing literature on fairness in classification with abstention is limited, covering only coverage-based abstention models. In coverage-based abstention models, the target coverage is decided before-hand and is kept the same for all the groups. In contrast, cost-based approaches introduce a cost for abstention, which can cause uneven abstention rates between different groups, leading to an unfair system. We extend the independence and separation fairness criteria to consider abstention. We provide a model-agnostic in-processing algorithm to incorporate these constraints in the models. We demonstrate the algorithm’s efficacy by experimenting with two different cost-based abstain option classifiers. Additionally, we explore mixing constraints from independence and separation criteria into one model, which is impossible in a binary classification task.