A software framework that analyzes live voice streams in real time using deep learning to detect AI-generated voice cloning and alerts users before sensitive transactions occur.
All India Council for Technical Education (AICTE) · Cyber Security Cell · Software
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Decomposed from what the description asks for. Nothing added.
Voice Authenticity Analysis
Performs acoustic, spectral, and prosody analysis using deep learning to detect synthetic speech artifacts.
Real-Time Risk Scoring
Computes a dynamic confidence score indicating the probability of impersonation during live calls.
Alerting and Interaction
Sends warnings and recommends secondary verification when impersonation risk crosses set thresholds.
Privacy and Compliance
Reduces central audio storage through edge inference, minimal retention, and feature-only logging.
Integration APIs and SDKs
Provides REST or gRPC endpoints and SDKs to integrate with banking and communication systems.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check whether your model can process audio streams in near real time, whether it handles multiple Indian accents and dialects accurately, and how it integrates via APIs or SDKs.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
Specific latency threshold numbers that define real time?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Dataset of genuine and cloned Indian regional voices to use for training?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact threshold values for triggering risk alerts?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for voice authenticity analysis. How would you build that?
Decoded from SIH26104 itself — Performs acoustic, spectral, and prosody analysis using deep learning to detect synthetic speech artifacts. The brief asks for it by name.
The brief asks for real-time risk scoring. How would you build that?
Decoded from SIH26104 itself — Computes a dynamic confidence score indicating the probability of impersonation during live calls. The brief asks for it by name.
The brief asks for alerting and interaction. How would you build that?
Decoded from SIH26104 itself — Sends warnings and recommends secondary verification when impersonation risk crosses set thresholds. The brief asks for it by name.
The brief asks for privacy and compliance. How would you build that?
Decoded from SIH26104 itself — Reduces central audio storage through edge inference, minimal retention, and feature-only logging. The brief asks for it by name.
The brief asks for integration apis and sdks. How would you build that?
Decoded from SIH26104 itself — Provides REST or gRPC endpoints and SDKs to integrate with banking and communication systems. The brief asks for it by name.
Where does your data come from — a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check whether your model can process audio streams in near real time, whether it handles multiple Indian accents and dialects accurately, and how it integrates via APIs or SDKs.
This is the evaluator read for your problem statement, decoded from the brief's own wording.