Multilingual Speaker Diarization Using Language-Specific Embeddings
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Solution Overview
Problem
Existing speaker diarization models struggle to accurately identify speakers in multi-language conversations due to the different acoustic characteristics of various languages, leading to decreased accuracy when the same speaker speaks different languages.
Innovation Solution
A method and apparatus that utilize multiple neural networks trained on different languages to extract speaker embeddings, perform language-dependent clustering, and then combine these clusters based on vector dissimilarity to identify speakers across languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a general speaker embedding extraction model is used to process speech recordings regardless of language, then the device complexity is reduced, but the speaker identification accuracy decreases when the same speaker speaks different languages
Solution Approach 1:
The patent divides the speaker embedding extraction process into separate language-specific models. Each language has its own trained neural network that extracts embeddings tailored to that language's acoustic characteristics. This segmentation allows the system to maintain low overall complexity while achieving high accuracy for each language individually.
Solution Approach 2:
The patent changes the parameters of the embedding extraction process by training separate models on language-specific data. Each model learns language-dependent acoustic features and embedding dimensions optimized for its target language, thereby improving identification accuracy without requiring a single overly complex universal model.
2Measurement precision
If language-dependent speaker embedding extraction models are used, then the speaker identification accuracy improves, but the device complexity increases
Solution Approach 1:
The patent creates a universal framework that handles multiple languages through a standardized process: language identification, selective model invocation, embedding extraction, and clustering. While individual models are language-specific, the overall system architecture remains universal and manageable, balancing accuracy improvement with controlled complexity.
Data Source
AI summary
An apparatus for processing speech data may include a processor configured to: separate speech signals from an input speech; identify a language of each of the speech signals that are separated from the input speech; extract speaker embeddings from the speech signals based on the language of each of the speech signals, using at least one neural network configured to receive the speech signals and output the speaker embeddings; and identify a speaker of each of the speech signals by iteratively clustering the speaker embeddings.