Speech Representation Learning With Speaker Disentanglement
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Solution Overview
Problem
Existing self-supervised learning (SSL) methods struggle to disentangle speaker variations from speech content effectively, often leading to significant loss of valuable content information during the process.
Innovation Solution
A novel SSL framework, CONTENTVEC, employs a teacher module to obscure speaker information, a student module to enforce speaker invariance, and a speaker conditioning module to predict masked speech representations, using techniques like voice conversion and contrastive learning to minimize content loss while achieving speaker disentanglement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speaker information is removed to disentangle content, then speaker interference is reduced, but content information is lost
Solution Approach 1:
The speech signal is segmented into multiple frequency bands using mel-filterbanks, allowing the system to process and disentangle speaker characteristics across different frequency regions independently. This segmentation enables selective manipulation of speaker information while preserving content in specific frequency ranges.
Solution Approach 2:
Different parts of the speech spectrum are treated with different quality requirements. The system applies frequency-dependent processing where certain frequency bands are more heavily transformed for speaker disentanglement while others are preserved for content integrity. The loss function applies different weights to different frequency regions based on their importance for content versus speaker information.
Solution Approach 3:
The speaker disentangling process uses asymmetric transformations where the forward transformation (speaker removal) differs from the reverse transformation. The system applies non-symmetric operations in the frequency domain that selectively remove speaker characteristics while maintaining content structure, recognizing that perfect reversibility is not required for the disentangled representation.
2Reliability
If random transformations are applied to alter speaker information, then speaker disentanglement is improved, but speech structure may be distorted
Solution Approach 1:
The system employs dynamic transformations that adapt to the input speech characteristics. The random transformations are not fixed but vary based on the specific speech utterance, allowing the system to maintain speech structure while effectively altering speaker information. The transformations are dynamically adjusted to preserve temporal and spectral relationships essential for speech structure.
Solution Approach 2:
The system changes specific parameters of the speech signal (frequency scaling, time stretching, pitch shifting) while keeping other parameters relatively stable. By selectively modifying certain acoustic parameters and preserving others, the system achieves speaker disentanglement without distorting the overall speech structure. The parameter changes are controlled to maintain the naturalness and intelligibility of the speech.
3Productivity
If partial masking is applied to generate speech representations, then content prediction is enabled, but speaker information may be obscured
Solution Approach 1:
The system applies partial masking to only certain portions of the speech signal rather than complete masking. This partial action allows the model to predict masked content while retaining enough information to preserve speaker characteristics. The masking is applied selectively to achieve the right balance between enabling content prediction and maintaining speaker information for subsequent disentanglement.
Data Source
AI summary
A method, computer system and computer program product is presented for providing a self-supervised speech representation. In one embodiment, audio input is received including speech utterances. A label sequence is generated from these speech utterances by a teacher label generator. A speech representation is generated of a partially masked version of the speech utterance using a speech representation network. The speech utterance is passed into two random transformations that alter only speaker information prior to the partial masking. A predictor will then predict the label sequence. In one embodiment performance-based assessment is made on a cross-entropy loss between the generated label sequence and a predicted label sequence.