Phase 5: Speech Foundations · ~45 minutes · Python
Speaker Recognition & Verification
ASR asks "what did they say?" Speaker recognition asks "who said it?" The math looks the same — embeddings plus cosine — but every production decision hinges on a single EER number.
What you will learn
- Implement speaker recognition & verification from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
- Type: Build
- Languages: Python
- Prerequisites: Spectrograms, Mel Scale & Audio Features, Embedding Models — The 2026 Deep Dive
- Time: ~45 minutes
The Problem
A user says a passphrase. You want to know: is this the person they claim to be (verification, 1:1), or is it the first person in your enrollment bank (identification, 1:N)? Or neither — is this an unknown speaker (open-set)?
Pre-2018: GMM-UBM + i-vectors. Reasonable EER but fragile to channel shift (phone vs laptop) and emotion. 2018–2022: x-vectors (TDNN backbone trained with angular margin). 2022+: ECAPA-TDNN and WavLM-large embeddings. By 2026 the field is dominated by three models and one metric.
The metric is EER — Equal Error Rate. Set your decision threshold so False Accept Rate = False Reject Rate. The crossover is EER. Used in every paper, every leaderboard, every procurement call.
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