Target Speaker Audio Enhancement With Semi-Supervised Training
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Solution Overview
Problem
Existing target speaker extraction systems require large amounts of labeled audio data samples, which are difficult to obtain and may not provide enough variety in terms of target speakers, limiting the robustness of machine learning models.
Innovation Solution
A semi-supervised training method that combines labeled and noisy audio data samples, using supervised training with synthetic mixtures and semi-supervised training with speaker embeddings to enhance target speaker extraction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of labeled audio data are used for training, then model accuracy improves, but data acquisition difficulty and cost increase
Solution Approach 1:
The system performs preliminary action by generating synthetic labeled audio data through mixing clean speaker recordings with noise recordings before actual model training. This pre-generated synthetic data serves as training material, eliminating the need to collect and label large amounts of real-world noisy audio data manually.
Solution Approach 2:
The system creates copies of clean speaker recordings by mixing them with noise recordings to generate synthetic labeled audio data. These synthetic copies simulate real-world conditions while maintaining known ground truth labels, providing training data without requiring actual field collection.
2Reliability
If labeled audio data with known target speakers is used for training, then model training effectiveness improves, but speaker variety and robustness decrease
Solution Approach 1:
The system makes the training process universal by implementing a two-stage approach that first trains on synthetic data with controlled labels, then fine-tunes on real noisy data. This multi-functional training pipeline allows the model to learn from both idealized synthetic scenarios and diverse real-world conditions, achieving both effectiveness and adaptability.
Solution Approach 2:
The system introduces dynamics by transitioning from static synthetic data training to dynamic real-world noisy data training. The model adapts its learning process through semi-supervised learning on noisy data without reliable labels, allowing it to handle varying speaker characteristics and environmental conditions.
3Productivity
If supervised training with synthetic mixtures is used, then training efficiency improves, but real-world extraction performance may decrease
Solution Approach 1:
The system ensures continuity of useful action by seamlessly connecting supervised training on synthetic data with semi-supervised training on real noisy data. The model continuously learns and adapts from synthetic labeled data to real noisy data without interruption, maintaining training effectiveness while improving real-world performance through progressive learning.
Data Source
AI summary
Training a machine learning model for application to an audio enhancement system for a target speaker may be performed. When at least one clean audio speech sample of a target speaker is captured, the machine learning model may then be trained using noisy audio speech samples in which the voice of the target speaker is present in addition to the voices of other speakers and/or background noise. Once the machine learning model is sufficiently trained, it may be deployed for use in audio enhancement and voice processing for an audio transmission service.