Multi-Discriminator CycleGAN for Speech Domain Adaptation
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Solution Overview
Problem
Current unsupervised non-parallel speech domain adaptation methods, such as CycleGAN, perform poorly in speech domain adaptation applications, often producing incorrect results or collapsing to an identity mapping function, failing to effectively adapt speech recognition models across different speaker domains without supervised training data.
Innovation Solution
A multi-discriminator CycleGAN approach is introduced, which divides spectrogram representations into frequency bands and uses multiple discriminator models to perform discrimination during training, capturing variations in audio frequency and other domain variables, allowing for robust unsupervised adaptation of speech signals between different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single discriminator is used in CycleGAN for speech domain adaptation, then the model complexity is low, but the adaptation performance deteriorates due to inability to capture frequency band variations
Solution Approach 1:
The discriminator is divided into multiple frequency band-specific discriminators, where each discriminator processes a specific frequency range of the spectrogram. This segmentation allows the system to capture detailed variations in different frequency bands independently, significantly improving speech domain adaptation performance while managing complexity through modular architecture
2Measurement precision
If multiple discriminators are used to capture frequency variations, then the adaptation accuracy improves, but the training stability deteriorates due to potential collapse to identity mapping
Solution Approach 1:
Each discriminator is specialized to process specific frequency bands with local quality tailored to that band's characteristics. This local specialization allows precise capture of frequency-specific variations while maintaining training stability through distributed discrimination responsibilities across multiple specialized components
Solution Approach 2:
The system employs dynamic gradient manipulation mechanisms including gradient clipping and adaptive learning rates to maintain training stability. The gradient flow is dynamically controlled to prevent collapse to identity mapping while allowing the multiple discriminators to effectively learn frequency-specific domain variations
3Reliability
If supervised training data is used for speech domain adaptation, then the adaptation performance is high, but the requirement for parallel aligned data increases complexity and reduces applicability
Solution Approach 1:
The system uses unsupervised domain adaptation where the model learns to adapt speech domains without requiring supervised parallel aligned data. The multiple frequency band discriminators enable the system to self-service by capturing domain variations directly from unlabeled target domain data, eliminating the need for external supervision while maintaining high adaptation performance
Data Source
AI summary
A system for domain adaptation includes a domain adaptation model configured to adapt a representation of a signal in a first domain to a second domain to generate an adapted presentation and a plurality of discriminators corresponding to a plurality of bands of values of a domain variable. Each of the plurality of discriminators is configured to discriminate between the adapted representation and representations of one or more other signals in the second domain.