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Quick answer: speaker identification answers the question of who is speaking. It compares a voice sample against a set of enrolled voiceprints and returns the closest match, a 1:N search. It differs from speaker verification, which is a 1:1 check of a claimed identity.

Speaker Identification, a subset of speaker recognition, is the technology that determines the identity of an unknown speaker by comparing their voice characteristics with the voice characteristics of known speakers. Thus, it’s also known as Speaker Search or Speaker Spotting. This article explains what Speaker Identification, how it identifies speakers, and how it differs from Speaker Verification.

What does Speaker Identification do?

Speaker Identification matches a voice sample of an unknown user with voice IDs of known users. Imagine a business meeting with tens of participants. A Speaker Recognition engine can identify speakers as they speak. It offers convenience as it spots speakers even if they don’t use separate devices or log in with employee credentials. It enables a wide range of applications across industries, such as

  • Healthcare: Telehealth applications identify patients with chronic conditions and store their information in their medical records using Speaker Identification as they report their status.
  • Law Enforcement: Speaker Identification enables law enforcement agencies to identify suspects by comparing voice recordings from crime scenes vs. known voice samples.
  • Customer Service: Agents or IVRs provide personalized experiences and tailor their interactions to individuals by identifying a customer's voice.
Image depicts how Speaker identification, known as Speaker Search or Speaker Spotting, works: Speaker Recognition engine analyzes the voice sample and returns a probability of matches from the voiceprint database.

How does Speaker Identification work?

Speaker Recognition engines create voice IDs (enrollment) and compare them with existing voice IDs (matching). An engine

  1. analyzes the new voice sample
  2. predicts the likelihood of a match between the sample and voice IDs,
  3. returns scores that show the likelihood of the match or just a list of potential matches

Speaker Identification vs. Speaker Verification

Speaker Identification and Speaker Verification are both subsets of Speaker Recognition. If a speaker claims an identity, Speaker Recognition engines use the voice sample to confirm (verify) the claimed identity. Hence, it’s called Speaker Verification. If a speaker doesn’t claim an identity, Speaker Recognition engines use the voice sample to find (identify) the match within multiple (possibly many) voice IDs. Hence, it’s called Speaker Identification.

In a nutshell, a Speaker Recognition engine does a one-to-one match for Speaker Verification and a one-to-many match for Speaker Identification. Speaker Identification can return a list of alternatives with various probabilities, while Speaker Verification does not.

Picovoice’s Speaker Recognition, Eagle, comes with high accuracy and cross-platform support. Anyone can start building with Eagle Speaker Recognition for free and start identifying speakers in minutes.

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Frequently Asked Questions

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What is the difference between speaker identification and speaker verification?
Speaker identification is a 1:N search: which of the enrolled speakers is talking? Speaker verification is a 1:1 check: is this person who they claim to be? Verification powers voice biometrics and authentication; identification powers personalization and speaker search.
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Does speaker identification require a passphrase?
It’s optional with text-independent speaker identification. For example, Eagle Speaker Recognition enrolls and identifies speakers from natural speech, in any language, without a fixed phrase. However, text-dependent speaker identification requires a passphrase.
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What is the difference between speaker identification and speaker diarization?
Diarization separates a recording into who spoke when without knowing who the speakers are. Identification names the speaker by matching against enrolled voiceprints. Combined, they produce transcripts labeled with real names. Learn more about the difference between speaker diarization and speaker identification and use them together, as in the speaker identification across meetings cookbook recipe.