arXiv subjects

Bipasha Kashyap

Publications and source records attributed to Bipasha Kashyap.

7 recordsLinked to original sources

arXiv2026

Quantifying Dimensional Independence in Speech: An Information-Theoretic Framework for Disentangled Representation Learning

Speech signals encode emotional, linguistic, and pathological information within a shared acoustic channel; however, disentanglement is typically assessed indirectly through downstream task performance. We introduce an information-theoretic framework to quantify cross-dimension statistical dependence in handcrafted acoustic features by integrating bounded neural mutual information (MI) estimation with non-parametric validation. Across six corpora, cross-dimension MI remains low, with tight estimation bounds ($< 0.15$ nats), indicating weak statistical coupling in the data considered, whereas Source--Filter MI is substantially higher (0.47 nats). Attribution analysis, defined as the proportion of total MI attributable to source versus filter components, reveals source dominance for emotional dimensions (80\%) and filter dominance for linguistic and pathological dimensions (60\% and 58\%, respectively). These findings provide a principled framework for quantifying dimensional independence in speech.

cs.SD↗

arXiv2026

Geometric Analysis of Speech Representation Spaces: Topological Disentanglement and Confound Detection

Speech-based clinical tools are increasingly deployed in multilingual settings, yet whether pathological speech markers remain geometrically separable from accent variation remains unclear. Systems may misclassify healthy non-native speakers or miss pathology in multilingual patients. We propose a four-metric clustering framework to evaluate geometric disentanglement of emotional, linguistic, and pathological speech features across six corpora and eight dataset combinations. A consistent hierarchy emerges: emotional features form the tightest clusters (Silhouette 0.250), followed by pathological (0.141) and linguistic (0.077). Confound analysis shows pathological-linguistic overlap remains below 0.21, which is above the permutation null but bounded for clinical deployment. Trustworthiness analysis confirms embedding fidelity and robustness of the geometric conclusions. Our framework provides actionable guidelines for equitable and reliable speech health systems across diverse populations.

cs.SD↗

arXiv2026

Quantifying Quanvolutional Neural Networks Robustness for Speech in Healthcare Applications

Speech-based machine learning systems are sensitive to noise, complicating reliable deployment in emotion recognition and voice pathology detection. We evaluate the robustness of a hybrid quantum machine learning model, quanvolutional neural networks (QNNs) against classical convolutional neural networks (CNNs) under four acoustic corruptions (Gaussian noise, pitch shift, temporal shift, and speed variation) in a clean-train/corrupted-test regime. Using AVFAD (voice pathology) and TESS (speech emotion), we compare three QNN models (Random, Basic, Strongly) to a simple CNN baseline (CNN-Base), ResNet-18 and VGG-16 using accuracy and corruption metrics (CE, mCE, RCE, RmCE), and analyze architectural factors (circuit complexity or depth, convergence) alongside per-emotion robustness. QNNs generally outperform the CNN-Base under pitch shift, temporal shift, and speed variation (up to 22% lower CE/RCE at severe temporal shift), while the CNN-Base remains more resilient to Gaussian noise. Among quantum circuits, QNN-Basic achieves the best overall robustness on AVFAD, and QNN-Random performs strongest on TESS. Emotion-wise, fear is most robust (80-90% accuracy under severe corruptions), neutral can collapse under strong Gaussian noise (5.5% accuracy), and happy is most vulnerable to pitch, temporal, and speed distortions. QNNs also converge up to six times faster than the CNN-Base. To our knowledge, this is a systematic study of QNN robustness for speech under common non-adversarial acoustic corruptions, indicating that shallow entangling quantum front-ends can improve noise resilience while sensitivity to additive noise remains a challenge.

cs.SD↗

arXiv2025

Leveraging Vision Transformers for Enhanced Classification of Emotions using ECG Signals

Biomedical signals provide insights into various conditions affecting the human body. Beyond diagnostic capabilities, these signals offer a deeper understanding of how specific organs respond to an individual's emotions and feelings. For instance, ECG data can reveal changes in heart rate variability linked to emotional arousal, stress levels, and autonomic nervous system activity. This data offers a window into the physiological basis of our emotional states. Recent advancements in the field diverge from conventional approaches by leveraging the power of advanced transformer architectures, which surpass traditional machine learning and deep learning methods. We begin by assessing the effectiveness of the Vision Transformer (ViT), a forefront model in image classification, for identifying emotions in imaged ECGs. Following this, we present and evaluate an improved version of ViT, integrating both CNN and SE blocks, aiming to bolster performance on imaged ECGs associated with emotion detection. Our method unfolds in two critical phases: first, we apply advanced preprocessing techniques for signal purification and converting signals into interpretable images using continuous wavelet transform and power spectral density analysis; second, we unveil a performance-boosted vision transformer architecture, cleverly enhanced with convolutional neural network components, to adeptly tackle the challenges of emotion recognition. Our methodology's robustness and innovation were thoroughly tested using ECG data from the YAAD and DREAMER datasets, leading to remarkable outcomes. For the YAAD dataset, our approach outperformed existing state-of-the-art methods in classifying seven unique emotional states, as well as in valence and arousal classification. Similarly, in the DREAMER dataset, our method excelled in distinguishing between valence, arousal and dominance, surpassing current leading techniques.

eess.SP↗

arXiv2025

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation

Federated learning (FL) has emerged as a promising paradigm in machine learning, enabling collaborative model training across decentralized devices without the need for raw data sharing. In FL, a global model is trained iteratively on local datasets residing on individual devices, each contributing to the model's improvement. However, the heterogeneous nature of these local datasets, stemming from diverse user behaviours, device capabilities, and data distributions, poses a significant challenge. The inherent heterogeneity in federated learning gives rise to various issues, including model performance discrepancies, convergence challenges, and potential privacy concerns. As the global model progresses through rounds of training, the disparities in local data quality and quantity can impede the overall effectiveness of federated learning systems. Moreover, maintaining fairness and privacy across diverse user groups becomes a paramount concern. To address this issue, this paper introduces a novel FL framework, ClusterGuardFL, that employs dissimilarity scores, k-means clustering, and reconciliation confidence scores to dynamically assign weights to client updates. The dissimilarity scores between global and local models guide the formation of clusters, with cluster size influencing the weight allocation. Within each cluster, a reconciliation confidence score is calculated for individual data points, and a softmax layer generates customized weights for clients. These weights are utilized in the aggregation process, enhancing the model's robustness and privacy. Experimental results demonstrate the efficacy of the proposed approach in achieving improved model performance in diverse datasets.

cs.LG↗

arXiv2025

Quantum Approaches for Dysphonia Assessment in Small Speech Datasets

Dysphonia, a prevalent medical condition, leads to voice loss, hoarseness, or speech interruptions. To assess it, researchers have been investigating various machine learning techniques alongside traditional medical assessments. Convolutional Neural Networks (CNNs) have gained popularity for their success in audio classification and speech recognition. However, the limited availability of speech data, poses a challenge for CNNs. This study evaluates the performance of CNNs against a novel hybrid quantum-classical approach, Quanvolutional Neural Networks (QNNs), which are well-suited for small datasets. The audio data was preprocessed into Mel spectrograms, comprising 243 training samples and 61 testing samples in total, and used in ten experiments. Four models were developed (two QNNs and two CNNs) with the second models incorporating additional layers to boost performance. The results revealed that QNN models consistently outperformed CNN models in accuracy and stability across most experiments.

cs.ET↗

arXiv2024

VR Based Emotion Recognition Using Deep Multimodal Fusion With Biosignals Across Multiple Anatomical Domains

Emotion recognition is significantly enhanced by integrating multimodal biosignals and IMU data from multiple domains. In this paper, we introduce a novel multi-scale attention-based LSTM architecture, combined with Squeeze-and-Excitation (SE) blocks, by leveraging multi-domain signals from the head (Meta Quest Pro VR headset), trunk (Equivital Vest), and peripheral (Empatica Embrace Plus) during affect elicitation via visual stimuli. Signals from 23 participants were recorded, alongside self-assessed valence and arousal ratings after each stimulus. LSTM layers extract features from each modality, while multi-scale attention captures fine-grained temporal dependencies, and SE blocks recalibrate feature importance prior to classification. We assess which domain's signals carry the most distinctive emotional information during VR experiences, identifying key biosignals contributing to emotion detection. The proposed architecture, validated in a user study, demonstrates superior performance in classifying valance and arousal level (high / low), showcasing the efficacy of multi-domain and multi-modal fusion with biosignals (e.g., TEMP, EDA) with IMU data (e.g., accelerometer) for emotion recognition in real-world applications.

eess.SP↗