Cross-Lingual Speaker Recognition With Language Compensation
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Solution Overview
Problem
Conventional voice biometric systems struggle to provide consistent speaker recognition across different languages, generating distinct results when speakers switch between languages.
Innovation Solution
Implement a machine-learning architecture with an embedding extraction engine and a multi-class language classifier to determine speaker similarity and language likelihood scores, using cross-lingual quality measures to adjust speaker verification scores and fine-tune the model with flip signal augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional voice biometric systems are used, then the system structure is simple, but speaker recognition accuracy deteriorates when speakers switch between languages
Solution Approach 1:
The system segments the speaker verification process into separate components: an embedding extraction engine for voiceprint generation, a language classifier for language detection, and a verification engine for speaker identification. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The language classifier acts as an intermediary component that detects the language of audio signals and provides language likelihood scores to the verification engine. This intermediary enables the system to compensate for language variations without requiring complete redesign of the core verification functionality.
2Adaptability or versatility
If the system processes audio signals in multiple languages, then language adaptability improves, but processing time increases
Solution Approach 1:
The system performs preliminary language detection using the language classifier on audio signals before proceeding to speaker verification. By pre-identifying the language and calculating language likelihood scores in advance, the verification engine can optimize its processing based on language information, reducing overall processing time while maintaining multi-language support.
3Measurement precision
If cross-lingual quality measures are applied to adjust verification scores, then speaker recognition accuracy improves, but computational complexity increases
Solution Approach 1:
The system changes the parameter space by introducing language likelihood scores and cross-lingual quality measures as additional dimensions for evaluation. The verification engine adjusts verification scores based on these new parameters, improving accuracy by accounting for language variations while managing computational complexity through efficient score adjustment mechanisms.
Data Source
AI summary
Disclosed are systems and methods including computing-processes executing machine-learning architectures for voice biometrics, in which the machine-learning architecture implements one or more language compensation functions. Embodiments include an embedding extraction engine (sometimes referred to as an “embedding extractor”) that extracts speaker embeddings and determines a speaker similarity score for determine or verifying the likelihood that speakers in different audio signals are the same speaker. The machine-learning architecture further includes a multi-class language classifier that determines a language likelihood score that indicates the likelihood that a particular audio signal includes a spoken language. The features and functions of the machine-learning architecture described herein may implement the various language compensation techniques to provide more accurate speaker recognition results, regardless of the language spoken by the speaker.