# How Computers Automatically Label Different Speakers in Audio Recordings > A method for identifying and labeling speakers in audio recordings, even if the system has never heard the person speak before, by grouping similar voices and asking a user to name them. - **Patent:** US 6424946 - **Original title:** Methods and apparatus for unknown speaker labeling using concurrent speech recognition, segmentation, classification and clustering - **Owner:** International Business Machines Corp - **Granted:** 2002 - **Status:** Public domain (expired) - **Times cited:** 75 - **Field:** software, ai_ml, telecommunications ## What it does This system processes audio to identify who is speaking by breaking the audio into segments and comparing them against a database. It uses 'background models' for people not yet in the system, such as a generic 'unenrolled male' or 'unenrolled female' profile. When the system detects a recurring voice that it doesn't recognize, it groups those segments together into a cluster. A user can then provide a name for that cluster, and the system automatically updates its database to recognize that person in future recordings. ## What it does NOT cover - Does not cover real-time voice synthesis or voice modification. - Does not cover hardware-based microphones or physical audio capture devices. - Does not cover methods that require every speaker to be pre-enrolled before identification can begin. - Does not cover the specific linguistic analysis of the words being spoken, only the identification of the speaker. ## The clever bit The system uses a 'background model' for unknown speakers, allowing it to track and cluster voices it hasn't learned yet, rather than simply failing to identify them or labeling them as noise. ## Real-world examples 1. Automated meeting transcription services like Otter.ai 2. Zoom and Microsoft Teams speaker identification features 3. Legal and medical transcription software 4. Call center analytics platforms ## Why it matters This patent laid the groundwork for modern transcription services that distinguish between multiple participants in a meeting. It solved the 'cold start' problem in voice recognition, where a system could not label a speaker until they were manually registered. This is now a standard feature in enterprise meeting software and legal transcription tools. ## Frequently asked questions ### What does How Computers Automatically Label Different Speakers in Audio Recordings cover? A method for identifying and labeling speakers in audio recordings, even if the system has never heard the person speak before, by grouping similar voices and asking a user to name them. ### Who owns patent US 6424946? International Business Machines Corp owns this patent, granted in 2002. ### When does this patent expire? This patent has expired and is now in the public domain — anyone can use the invention freely. ### What is patent US 6424946 cited by? This patent has been cited by 75 later patents that build on its ideas. ### What problem does this patent solve? This patent laid the groundwork for modern transcription services that distinguish between multiple participants in a meeting. It solved the 'cold start' problem in voice recognition, where a system could not label a speaker until they were manually registered. This is now a standard feature in enterprise meeting software and legal transcription tools. ### What does this patent NOT cover? Does not cover real-time voice synthesis or voice modification. **Full plain-English explainer:** https://patentbrief.org/patent/us/6424946/amazon-personalized-recommendations **Original patent:** https://patents.google.com/patent/US6424946 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How AI Predicts Who Will Speak Next in a Conversation](https://patentbrief.org/patent/us/11645473/palm-pathways-language-model) — IBM's patent describes a system that uses neural networks to analyze speech patterns and intentions to predict which person will talk next in a conversation. - [How AI Learns New Tasks Using Old Data Labels](https://patentbrief.org/patent/us/11062228/gpt-3-few-shot-learning) — A method for helping AI models understand new topics by grouping similar labels from different datasets into a shared, broader category. - [How AI Cleans Up Irrelevant Topics in Recorded Phone Calls](https://patentbrief.org/patent/us/11521601/stylegan) — A system that automatically identifies and removes 'noisy' or irrelevant topics from call center transcripts by analyzing how consistently and broadly those words appear. - [How Sonos Speakers Use Personalized Wake Words to Recognize Different Users](https://patentbrief.org/patent/us/9965247/icloud-drive) — A system that lets multiple people control a shared speaker by using unique voice-trigger words to link their specific music accounts and preferences. - [How Smart Speakers Know You're Talking to Them After a Command](https://patentbrief.org/patent/us/11361763/detecting-system-directed-speech) — This patent describes how a smart speaker system can tell if follow-up speech is meant for it, even without a "wake word," by analyzing voice activity and partial speech recognition results using an AI model.