Edge-Deployable Voice Biometric Authentication
Speaker verification at 2.17% EER, compressed to 1.57 MiB, running on the device itself.
Developed in collaboration with Analog Devices India, this project targets high-accuracy speaker verification on resource-constrained edge hardware: specifically the ARM Cortex-A55. The constraint is unforgiving: the entire pipeline, from feature extraction to verification decision, has to fit and run on-chip.
on the VoxCeleb1 test set
- Equal Error Rate (float)
- 2.17%
- EER after INT8 quantisation
- 2.60%
- Quantised model size
- 1.57 MiB
- Channels (ECAPA-Lite)
- 128
Summary
What the work set out to do, and the reasoning that shaped it.
The system is built on ECAPA-Lite, a 128-channel lightweight variant of the ECAPA-TDNN architecture, selected for its exceptional accuracy-to-size ratio: the property that matters most when the deployment target is measured in megabytes.
A significant innovation of this work is the addition of a Lightweight Prosody Encoder branch. This captures supra-segmental features: pitch, energy and duration: which significantly enhance the system's ability to discriminate between speakers. Prosody carries identity information that spectral features alone leave on the table.
The model achieved an Equal Error Rate of 2.17% on the VoxCeleb1 test set. To meet edge deployment requirements it was quantised to INT8 precision, reducing size to approximately 1.57 MiB while maintaining high accuracy at 2.60% EER: a 0.43 percentage point cost for a substantial compression gain.
Impact
What changed as a result.
Demonstrates that speaker verification competitive on a standard public benchmark can run entirely on edge hardware, with no network round-trip and no server-side inference.
The prosody encoder branch is a novel contribution: supra-segmental features are added at negligible parameter cost while measurably improving speaker discrimination.
The complete solution includes on-chip feature extraction: both MFCC and prosody: and was validated on the EV-SC598-SOM platform, proving feasibility for low-power, real-world biometric security.
Figures
The work, drawn.
Purpose-built diagrams: each one carries an idea that prose alone would take a page to deliver.
ECAPA-Lite architecture
A 128-channel lightweight ECAPA-TDNN variant chosen for accuracy-to-size ratio rather than headline accuracy alone.
The prosody encoder branch
Pitch, energy and duration run in parallel with the spectral path. Prosody encodes speaking style: information MFCCs discard.
The compression trade
INT8 quantisation costs 0.43 percentage points of EER and buys a model that fits in 1.57 MiB.
On-device pipeline
Feature extraction, embedding and verification all execute on the ARM Cortex-A55. Nothing leaves the device.
Methods
How it was done.
ECAPA-Lite backbone
A 128-channel lightweight variant of ECAPA-TDNN. The channel reduction is what brings the parameter count into edge range while preserving the architecture's attentive statistics pooling.
Lightweight Prosody Encoder
A parallel branch capturing supra-segmental features: pitch, energy, duration. These vary slowly relative to spectral content and encode speaking style rather than vocal tract shape, which is why they add discriminative power rather than duplicating it.
INT8 quantisation
Post-training quantisation to INT8 precision reduces the model to approximately 1.57 MiB. Accuracy degrades from 2.17% to 2.60% EER: a controlled and acceptable trade for the deployment envelope.
On-chip feature extraction
MFCC and prosody feature extraction both run on-device, so no part of the pipeline depends on off-chip preprocessing.
Hardware validation
Validated on the EV-SC598-SOM platform targeting the ARM Cortex-A55, establishing feasibility rather than merely simulating it.
Specifications
The numbers.
Development timeline
From simulation to the clinic.
Project sanctioned
Analog Devices, Inc. awards ₹6 Lakhs for a proof of concept in voice authentication for cars using an ML approach. Dr. Ravi serves as Principal Investigator.
ECAPA-Lite selection
A 128-channel lightweight ECAPA-TDNN variant chosen for its accuracy-to-size ratio under edge constraints.
Prosody encoder branch
A lightweight parallel branch added to capture pitch, energy and duration: supra-segmental cues that improve speaker discrimination.
2.17% EER on VoxCeleb1
Full-precision model evaluated on the VoxCeleb1 test set.
INT8 quantisation
Model reduced to ≈1.57 MiB at 2.60% EER, meeting the edge deployment envelope.
On-device validation
Complete pipeline with on-chip MFCC and prosody extraction validated on EV-SC598-SOM.
Outcomes
- ECAPA-Lite with a novel Lightweight Prosody Encoder branch
- 2.17% EER on VoxCeleb1 at full precision
- 2.60% EER at INT8, ≈1.57 MiB model size
- On-chip MFCC and prosody feature extraction
- Validated on EV-SC598-SOM targeting ARM Cortex-A55
Collaborators & support
- Analog Devices India
- Sri Sathya Sai Institute of Higher Learning
Funded under
Analog Devices, Inc. · INR 6 Lakhs
2025-2026 · Principal Investigator