Abstract

Stroke often results in motor speech disorders such as dysarthria, leading to reduced speech intelligibility and impaired communication. Accurate assessment of speech intelligibility and identification of
underlying articulatory deficits are essential for effective rehabilitation. However, existing approaches
largely rely on subjective evaluation or global scoring, offering limited support for targeted therapy
planning, particularly in under-resourced languages such as Telugu.
This work presents an objective and interpretable framework for assessing speech intelligibility and
articulatory impairments in Telugu-speaking post-stroke individuals. A phonemically structured speech
corpus was developed, comprising recordings from 13 stroke participants along with perceptual intelligibility ratings obtained from human evaluators. In addition, speech data from 25 age-matched healthy
participants were collected as part of a pilot study to establish a reference dataset. Word-level intelligibility was estimated using Dynamic Time Warping (DTW) applied to embeddings derived from the
Wav2Vec2-Large-XLSR-53-Telugu model, and validated against listener judgments.
To enable detailed analysis beyond global scoring, a minimal pairs based articulatory mapping framework was proposed. By analyzing phonological contrasts such as place of articulation and voicing, the
framework identifies consistent phoneme-level errors and maps them to probable articulatory subsystems. This approach provides a meaningful link between acoustic speech deviations and underlying
physiological mechanisms.
In addition, a combined analysis of word-level and passage-level speech was performed to capture
both articulatory precision and performance in conversational speech. The results highlight the heterogeneous nature of post-stroke speech impairment, with deficits often localized to specific phoneme
groups or arising from coordination challenges in continuous speech. The findings also emphasize the
importance of considering dialectal variation in speech assessment.
Beyond overall intelligibility assessment, the proposed framework enables phoneme-level analysis
of speech errors. By identifying consistently misarticulated phonemes and mapping them to underlying
articulatory subsystems, it provides interpretable insights that can support targeted therapy planning. In
summary, proposed approach employs phonemic-level assessment to identify speech impairments and
provides articulator specific insights, enabling therapists to design targeted interventions using phoneticlevel measures. This approach contributes toward the development of data driven and personalized
rehabilitation strategies for dysarthric speech in Telugu.

Abstract

This thesis examines polycrisis as a condition of interdependent and mutually reinforcing crises, focusing on the Western Himalayan region encompassing Himachal Pradesh and Uttarakhand. In recent
years, the term polycrisis has gained prominence to describe situations in which environmental, economic, political, and social stresses overlap in ways that blur causal boundaries and complicate policy
responses. While existing studies have extensively analysed individual crises in the Western Himalayas
such as climate change impacts, agricultural stress, migration, and disaster risk, these processes are often
examined in isolation. This thesis argues that such siloed approaches are insufficient for understanding
the region's systemic vulnerability.
The central contribution of this work is the development of a structured modeling framework that
represents polycrisis as an interconnected system rather than a collection of parallel challenges. Our
approach begins by identifying four main domains of environmental, economic, political and social
aspects of polycrisis. Within these, we select key variables (nodes) such as climate change, migration,
agricultural productivity, and disaster preparedness and governance; each broken down into concomitant
sub-factors (subnodes). Using an extensive review of peer-reviewed literature, government reports, and
region-specific case studies, the thesis systematically maps both first-order relationships between nodes
and the subnodes and second-order relationships between subnodes themselves. This two-level mapping
reveals feedback loops and cascading pathways that are often obscured in domain-specific analyses.
Across both mapping iterations, that is first-order relationships (node ↔ subnode) being the first iteration and second-order relationships (subnode ↔ subnode) being the second iteration, the thesis identifies and documents approximately 120 relationships, which are categorised using a five-point ordinal
scale ranging from Very Strong to Negligible. This categorisation is grounded in empirical evidence
and conceptual studies centered around Western Himalayas or comparable mountain ranges. The resulting network highlights the centrality of climatic variables, the mediating role of governance, and the
reinforcing interactions between agricultural stress and migration.
The framework serves as a proof of concept for simulating cascading effects across socio-ecological
systems and informing anticipatory policy responses in mountainous regions under stress by addressing
how multiple, co-occurring crises interact across environmental, economic, political, and social systems
to constitute a regional polycrisis, and proposes a systematic model to represent and quantify these
linkages.

Abstract

Wearable plantar pressure measurement systems have become important in biomechanics, rehabilitation, and gait analysis. By capturing foot-ground interaction data, these systems enable researchers and
clinicians to evaluate balance, diagnose pathological gait, design personalised footwear, and monitor patient recovery. Existing research in this domain has predominantly focused on extracting insights about
the human body, with applications centred on musculoskeletal health, motor control, and performance
assessment. However, despite the rich information embedded in plantar pressure signals, their potential
to characterise properties of the external environment, particularly the surfaces on which humans walk,
has received little to no attention.
This thesis addresses this unexplored dimension by investigating whether plantar pressure data can
be leveraged to infer surface characteristics, explicitly focusing on hardness. Unlike conventional approaches to surface analysis, which rely on specialised equipment or destructive testing, wearable plantar pressure systems provide a low-cost, portable, and unobtrusive means of measurement. The central
research question guiding this work is whether pressure patterns generated by the foot during locomotion can be systematically mapped to the mechanical properties of the underlying surface.
To explore this question, we developed a low-cost in-shoe sensor array capable of capturing plantar
pressure distributions in real time. Experiments were conducted by collecting walking trials across
surfaces of varying hardness, ranging from soft foam and natural ground to rigid concrete and tiled
flooring. The recorded plantar pressure profiles were then analysed to identify distinctive signatures
associated with different surface types. Statistical and machine learning techniques were employed to
classify surfaces based on the extracted features, enabling quantitative assessment of surface hardness
from wearable data.
The results demonstrate that surface hardness exerts a measurable influence on plantar pressure distribution, and that these differences can be reliably captured through the proposed in-shoe system. Our
findings establish, for the first time, that wearable plantar pressure measurements can be repurposed for
surface characterisation. This represents a conceptual shift in how wearable sensing technologies are
perceived, broadening their utility from human biomechanics to context-aware environmental interaction.
The implications of this work extend across several domains. In rehabilitation and sports science,
knowledge of surface properties can inform safer training and recovery protocols. In robotics, surface
characterisation through human-robot shared sensing could improve terrain adaptation strategies. From an urban perspective, large-scale deployment of such systems could support real-time monitoring of
ground conditions in public spaces. Overall, this thesis contributes a methodological innovation and a
new application space for wearable plantar pressure systems, bridging the gap between human-centered
sensing and surface characteristics.

Abstract

Memorability, the tendency for an item to be consistently remembered across individuals, is often
treated as an intrinsic property of stimuli, reflecting fixed characteristics of items themselves. However, models of cue-based retrieval predict that memorability should also depend on the functional role
an item plays during retrieval. Using a large cued-recall dataset in which each word served both as a
cue and as a target, we tested this prediction directly. Cue memorability showed substantially greater
cross-participant reliability than target memorability, indicating that the cognitive processes initiating retrieval are more consistent across individuals than those involved in recovering specific memory traces.
To identify the sources of this dissociation, we modeled cue and target memorability using a broad set
of psycholinguistic and semantic features. Number of senses, concreteness, and word frequency selectively supported cue effectiveness, whereas number of incoming associates and morphemic complexity
predicted target retrievability. However, at the level of semantic categories, cue and target memorability converged almost perfectly, consistent with prior work (1). This result suggests that category-level
structure captures stable conceptual regularities that shape memorability across retrieval roles. Together,
these findings suggest that memorability reflects an interaction between semantic structure and retrieval
processes, with distinct lexical properties supporting the effectiveness of items as cues versus their retrievability as targets

Abstract

Domain specific narrative texts are seen in education, governance, healthcare, legal and multiple
other domains. These texts encompass different kinds of general and domain-specific knowledge. To
make these niche narrative texts understandable and usable both by humans and machines, they need to
be processed through Natural Language Processing (NLP) mechanisms and made conducive for use in
desired domain specific applications. A specific kind of knowledge is spatial knowledge. It is useful for
enabling the fundamental cognitive skill of spatial understanding that underpins essential human interactions with the environment. Spatial knowledge enables one to perform basic navigation and orientation,
such as planning routes, understanding and explaining locations of objects and people, and describing
objects by their spatial features - shape, size, and appearance. Pre-trained Language Models (PLMs)
have fundamentally transformed NLP by serving as powerful, generalized language processors trained
on massive datasets. Their primary importance lies in achieving state-of-the-art performance across
diverse NLP tasks, often without the need for extensive task-specific retraining.
The work done as part of the thesis combines the above three aspects and comprises of automatic
extraction, representation and analysis of spatial knowledge in domain specific narratives with the help
of PLMs. Further, the work includes demonstration of the utility of the extracted knowledge in niche
use-cases. More specifically, the work is divided into two major aspects - PLMs as reasoners of spatial
knowledge and PLMs as containers of spatial knowledge. As part of the first aspect, we consider
an existing knowledge representation, the Message Sequence Chart (MSC) and enrich it with spatial
knowledge through a novel task of motion reasoning. It is through this task of motion reasoning, we
check how PLMs function as reasoner of dynamic spatial knowledge. As part of the second aspect, we
gauge PLMs for their familiarity of a specific kind of spatial knowledge - facts in world geography. We
construct a moderately sized dataset of facts related to 24 different geographical relations and check
multiple encoder and decoder PLMs through a mask filling exercise. Additionally, we enrich these
PLMs through fine-tuning to enhance their understanding of this particular kind of spatial knowledge.
The major research contributions of the thesis include (i) proposition of the MSC as a narrative
representation formalism, (ii) enrichment of the MSC with spatial knowledge for enabling it as a spatiotemporal knowledge representation, (iii) proposition of the novel task of motion reasoning under the
sub-area of spatial reasoning, (iv) the masked sentences dataset for the effort on gauging geography
knowledge in PLMs and its enrichment, and (v) proposition of novel applications namely crime report
gap finding, enhancing spatial fidelity in the filmstrip representation, niche tourism search and wikidata
augmentation. For several of these contributions, the developed datasets and code have been made available publicly for reproducibility as well as encouraging further research. Furthermore, a subset of
work in the thesis was carried out as part of a collaboration project between my organization - TCS
Research and IIIT - Hyderabad. The code/data artifacts and learning obtained as part of the PhD have
been employed in several Proof-of-Concepts (PoCs) developed and showcased for clients and internal
teams at TCS.

Abstract

The detection of a stochastic gravitational wave (GW) background would provide a direct probe of
the early universe, offering unique insights into the high-energy physics of the inflationary era. This
thesis investigates the production of primordial gravitational waves during the post-inflationary reheating period, specifically focusing on the graviton bremsstrahlung accompanying the perturbative decay
of the inflaton into pairs of massive spin-3/2 particles. Such spin-3/2 fields, described by the RaritaSchwinger formalism, are physically motivated candidates for dark matter and arise naturally in supergravity theories as gravitinos.
Modeling the inflaton as a classical field coherently oscillating in a polynomial potential of the form
V (ϕ) ∼ ϕ
k
(with k ≥ 2), we derive the decay rates for both the standard two-body decay (ϕ → ψµψµ)
and the three-body gravitational bremsstrahlung (ϕ → ψµψµhµν). We perform a fully numerical analysis of the reheating dynamics by solving the coupled Boltzmann equations for the inflaton, radiation,
and gravitational wave energy densities.
Our results demonstrate that the resulting GW spectrum exhibits a characteristic multi-peak structure,
which arises from the distinct contributions of the inflaton's harmonic oscillation modes. We identify
a hierarchy in the spectral contributions of these harmonics that is preserved across different potential
shapes. Although the predicted signal amplitude currently lies below the sensitivity curves of proposed
detectors such as the Einstein Telescope (ET) and Big Bang Observer (BBO), the spectral features offer
a theoretical framework for constraining the microscopic physics of inflation and the properties of the
hidden sector.

Abstract

Automatic Speech Recognition (ASR) has witnessed rapid progress with the advent of self-supervised
learning and large-scale multilingual models. However, ASR for morphologically complex and lowresource languages remains challenging due to limited labeled data, orthographic inconsistencies, tokenization difficulties, and the computational demands of large-scale systems. Perso-Arabic languages
such as Persian, Arabic, and Urdu exemplify these difficulties, where script variations, rich morphology, complex word formation, and inconsistent text normalization complicate both preprocessing and
subword modeling for ASR systems.
Current state-of-the-art approaches largely rely on scaling model size and data volume, often overlooking the importance of linguistic structure, script-aware tokenization, and domain relevance. For
low-resource settings, scale-driven strategies are computationally prohibitive and frequently suboptimal.
This thesis investigates whether efficient and competitive ASR performance can instead be achieved
through linguistically informed preprocessing, morphologically-aware tokenization, and targeted crosslingual continual pretraining, without dependence on excessively large models.
We first introduce a unified script-aware preprocessing pipeline tailored to Perso-Arabic languages,
addressing normalization, tokenization, phonetic parsing, and text cleaning to reduce orthographic noise
and improve data consistency. Building upon this foundation, we construct a scalable multilingual unlabeled speech corpus of approximately 3,000 hours across Persian, Arabic, and Urdu. Using this corpus,
we systematically evaluate continual pretraining strategies under different initialization conditions and
integrate SentencePiece-based subword modeling within a CTC-based Wav2Vec framework, carefully
adapting token segmentation to handle word boundaries and repeated character collapse. All downstream fine-tuning and evaluation are conducted in a monolingual setting to isolate transfer effects.
Our results demonstrate that a 300M parameter model, when adapted through targeted continual
pretraining and morphologically-aware tokenization, achieves performance competitive with systems
over five times larger. Notably, the proposed model outperforms Whisper Large v3 on Persian and yields
strong results on Arabic and Urdu despite using substantially fewer parameters and labeled resources.
These findings challenge the prevailing assumption that ASR quality scales primarily with model
size. Instead, we show that data relevance, adaptation strategy, and linguistically informed tokenization
play a more critical role in low-resource scenarios. This thesis provides a practical and resource-efficient
pathway toward competitive ASR for Perso-Arabic languages and offers a reproducible experimental
template that may inform future multilingual extensions.

Abstract

Modeling realistic human-object interaction in three-dimensional environments is a fundamental
challenge in computer vision, with broad implications for robotics, virtual reality, and embodied artificial intelligence. Despite significant progress in human motion generation and 3D scene understanding,
existing approaches often treat motion, interaction, and environment as separate problems, limiting their
ability to generate physically plausible and context-aware behavior.
This thesis addresses this challenge through a unified perspective on scene-aware human-object interaction, grounded in both real-world systems and data-driven modeling. We begin by developing a
system for real-time human pose understanding and interaction analysis based on joint-level reasoning,
formalized in our published patent application. This system demonstrates how structured representations of human motion can enable robust, interpretable, and deployable interaction-aware applications.
Building on this foundation, we introduce a large-scale dataset and benchmark for full-body human
motion with object interaction in realistic 3D scenes. This dataset enables the study of scene-aware
grasping and exposes key limitations of existing methods in handling interaction under environmental
constraints. We then propose a learning-based framework for generating physically plausible, sceneaware human-object interactions that explicitly models the interplay between body motion, object manipulation, and scene geometry.
To enable scalable deployment of such systems, we further develop a fast and robust geometry processing framework based on feature-aware mesh simplification, which preserves interaction-relevant
geometric properties while significantly reducing computational complexity.
We also explore the extension of interaction-aware modeling to conditional settings, including multimodal human motion generation. In particular, we highlight the role of object interaction in ensuring
consistency between generated motion and environmental constraints, demonstrating that naive integration of motion and interaction leads to physically implausible results.
Through extensive experiments and evaluations, we demonstrate that the proposed approaches significantly improve the realism, physical plausibility, and scalability of human-object interaction in 3D
environments. By bridging real-world systems, data-driven modeling, and geometry processing, this
thesis contributes towards the development of integrated frameworks for human-centric 3D understanding and interaction.

Abstract

Classical motion planning for robotic manipulation includes a set of general algorithms that aim to
minimize a scene-specific cost of executing a given plan. This approach offers remarkable adaptability,
as they can be directly used off-the-shelf for any new scene without needing specific training datasets.
However, without a prior understanding of what diverse valid trajectories are and without specially
designed cost functions for a given scene, the overall solutions tend to have low success rates within a
certain time limit. Conversely, deep-learning-based planners achieve high accuracy within their training
domains but are notoriously brittle when faced with out-of-distribution environments. To bridge this
gap, we first introduce Ensemble-of-costs-guided Diffusion for Motion Planning (EDMP) [2]. EDMP
leverages a diffusion model to learn a prior over kinematically valid trajectories while incorporating
scene-specific "collision-cost" guidance directly at the time of inference. By employing an ensemble of
multiple cost functions to capture diverse scene nuances, EDMP significantly improves success rates and
generalizes to novel tasks, such as manipulating arbitrary held objects, without retraining.
Despite its robustness, EDMP, and similar waypoint-based diffusion planners, faces a fundamental
bottleneck: the requirement for a substantial number of denoising steps, which leads to slow inference
speeds. To address this, we propose Guided Polynomial Diffusion for Motion Planning (GPD)[1],
where we introduce diffusion in the parametric space of trajectories, where the parameters are
represented as Bernstein coefficients. We show that this representation greatly improves the effectiveness
of the cost-function guidance and the inference speed. We also introduce a novel stitching algorithm
that leverages the diversity in diffusion-generated trajectories to produce collision-free trajectories
with just a single cost function-guided model. We demonstrate that our approaches outperform SOTA
diffusion-based motion planners for manipulators and provide an ablation study on key components.

Abstract

Binocular Vision Anomalies (BVA) represent a significant public health concern, particularly affecting children and impacting their quality of life, educational performance, and daily functioning.
Traditional diagnostic methods for BVA, while clinically effective, present considerable barriers to
widespread implementation due to their high costs, requirement for specialized training, and limited
accessibility, especially in resource-constrained settings such as rural areas in developing countries.
This thesis presents the development, validation, and clinical implementation of VR-Phore, a novel
Virtual Reality-based diagnostic system designed to assess binocular vision anomalies using the established haploscopic principle. VR-Phore represents a paradigm shift from conventional diagnostic tools
like the synoptophore, offering a cost-effective, portable, and user-friendly alternative that maintains
clinical accuracy while enhancing accessibility.
The research encompasses a comprehensive investigation into the fundamentals of binocular vision
anomalies and critically evaluates existing measurement methodologies, identifying key limitations in
current diagnostic approaches. The thesis details the systematic design and development of multiple
VR Head-Mounted Display (HMD) systems, including implementations for Oculus devices and mobile
phone-based platforms, each optimized for specific clinical environments and resource availability.
Extensive experimental validation was conducted through carefully designed studies, beginning
with pilot investigations and progressing to comprehensive assessments involving children with normal binocular vision and those presenting with various binocular anomalies. The clinical validation
phase involved direct comparison between VR-Phore measurements and gold-standard synoptophore
assessments, demonstrating the system's diagnostic accuracy and reliability.
The research demonstrates that VR-Phore not only matches the diagnostic accuracy of traditional
methods but also provides enhanced precision through finer angular control (0.1 degrees) and improved
patient comfort through natural head movement accommodation.
The findings of this research establish VR-Phore as a clinically validated, accessible diagnostic tool
that addresses the critical gap in binocular vision assessment, particularly for underserved populations.
The system's adaptability for telemedicine applications and scalable deployment across diverse healthcare settings positions it as a transformative solution for early detection and management of binocular
vision anomalies. This work contributes significantly to the field of vision science by demonstrating
how emerging technologies can be leveraged to democratize access to specialized healthcare diagnostics while maintaining clinical rigor and accuracy