Speaker Diarization Using Embeddings to Isolate Overlapping Speech

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Solution Overview

Problem

Existing speaker diarization systems fail to accurately recognize multiple speakers in a given segment, leading to inaccurate diarization results that propagate errors and impact downstream applications, due to issues in speech segmentation, audio embedding, and clustering.

Innovation Solution

Utilize a trained generative model, such as a recurrent neural network (RNN) with memory layers like LSTM or GRU, to process audio data and generate speaker embeddings, refining the audio data to isolate utterances of a single speaker by using a trained generative model with dilated causal convolutional layers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional speaker diarization systems use speech segmentation, audio embedding extraction, and clustering steps, then the system can process audio data through a structured pipeline, but the system fails to accurately recognize multiple speakers in overlapping segments and errors propagate through each step

Engineering Contradiction:

Improveaccuracy of speaker recognitionVSAvoidcomplexity of diarization pipeline

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts and removes other speakers' voices from the audio segment containing the target speaker, leaving only the target speaker's voice. This is achieved by generating speaker embeddings for the target speaker and using a trained generative model to synthesize audio containing only the target speaker's utterances, effectively extracting the desired component while eliminating interference from other speakers.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent introduces speaker embeddings as an intermediary representation that captures speaker-specific characteristics. These embeddings serve as a bridge between the raw audio input and the final separated output, enabling the system to identify and isolate specific speakers without requiring complex direct analysis of overlapping speech segments.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If the system attributes each segment to only one speaker or fails to attribute it, then the processing remains simple, but the diarization accuracy deteriorates when multiple speakers speak during a segment

Engineering Contradiction:

Improveprecision of speaker attributionVSAvoidsimplicity of speaker assignment

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent replaces the traditional mechanical clustering approach with a generative model-based synthesis approach. Instead of mechanically assigning segments to speakers through clustering algorithms, the system uses a trained generative model to synthesize audio segments for each speaker, automatically achieving precise attribution without complex assignment logic.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If speech segmentation uses long segments to improve resolution, then fewer segments are produced, but the resolution within segments decreases and accuracy suffers

Engineering Contradiction:

Improveresolution of speech segmentsVSAvoidnumber of segments

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent maintains continuous speaker identification across segment boundaries by using speaker embeddings that capture global speaker characteristics. This allows the system to process audio in manageable segments while preserving accurate speaker attribution continuity, eliminating the need to choose between long low-resolution segments or many short segments.

Inventive Principle:

Principle #20Continuity of useful action

4Productivity

If audio embeddings are generated locally without global information, then processing is faster and simpler, but errors are introduced that propagate to subsequent steps

Engineering Contradiction:

Improveprocessing speedVSAvoidaccuracy of audio embedding

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent performs preliminary generation of speaker embeddings from the entire audio input before processing individual segments. This preliminary action ensures that global speaker information is captured upfront, and subsequent local processing can rely on these pre-computed embeddings, maintaining both speed and accuracy without requiring repeated global analysis.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12548562B2Speaker diarization using speaker embedding(s) and trained generative model

Publication Date: 2026.02.10 GOOGLE LLC

AI summary

Speaker diarization techniques that enable processing of audio data to generate one or more refined versions of the audio data, where each of the refined versions of the audio data isolates one or more utterances of a single respective human speaker. Various implementations generate a refined version of audio data that isolates utterance(s) of a single human speaker by generating a speaker embedding for the single human speaker, and processing the audio data using a trained generative model—and using the speaker embedding in determining activations for hidden layers of the trained generative model during the processing. Output is generated over the trained generative model based on the processing, and the output is the refined version of the audio data.