100 beginner machine learning projects with runnable Python code, tests and honest case studies. By Networkers Home, Bangalore.
This small teaching project demonstrates a closed-set speaker-recognition pipeline using synthetic, opt-in audio-like signals. It shows how to: generate a simple multi-session dataset that mimics different speakers and recording sessions, extract lightweight spectral features, train a classifier baseline, and add an explicit unknown-speaker rejection policy based on prediction confidence. The goal is educational: to teach feature extraction, evaluation across sessions, and how to measure false accepts and rejects. This prototype is not suitable for any real biometric use.
The data are synthetic signals generated inside the code to mimic voice-like differences: each “speaker” is created by summing a few sinusoids with speaker-specific frequency harmonics, plus session-specific noise and small amplitude variation. There are multiple sessions per speaker to enable cross-session evaluation. This is intentionally synthetic and small so no real voice data or private biometric information is used.
Run the pipeline locally with: python project.py The script prints a small JSON object of evaluation metrics computed on a held-out test split.
Run the test suite with: pytest -q The tests check that the run function is deterministic, returns the expected keys, metrics are in valid ranges, and the held-out known-speaker accuracy meets a conservative floor.
Measured by running python project.py (seed 42) on 2026-10-23; the tests in test_project.py assert the floor.
| Metric | Value |
|---|---|
| overall_accuracy | 0.1000 |
| known_speaker_accuracy | 0.8000 |
| unknown_reject_rate | 0.8750 |
| false_accept_rate | 0.2500 |
| threshold | 0.7380 |
| holdout_session | 2 |
This project uses synthetic, non-speech audio-like signals and does not contain real voices, transcripts or any biometric data. The recognition task is a didactic closed-set classification with a simple rejection rule; it is not robust across real microphones, languages, or acoustic conditions. Do not use this prototype for access control, identification in the wild, or any deployment involving personal data.
Possible extensions include replacing synthetic signals with a properly consented small speech corpus, extracting richer features (MFCCs or learned embeddings), introducing more realistic noise and reverberation, and testing more principled open-set/thresholding strategies. Always follow privacy and consent best practice when working with real voice data.
Full case study, build steps and evaluation checks: Speaker recognition system on networkershome.com
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