Soft Alignment for Voice Conversion Using Probability
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Solution Overview
Problem
Conventional voice conversion systems rely on hard alignment techniques for vector transformation, which result in alignment errors, increased complexity, and inefficiency due to the one-to-one matching requirement between source and target vectors, making perfect alignment impossible and introducing noise into the data model.
Innovation Solution
Implementing a soft alignment scheme that allows for multiple vector pairs with associated alignment probabilities, enabling the generation of joint feature vectors and computation of transformation models using Expectation-maximization algorithms, thereby reducing alignment errors and improving efficiency and quality in vector transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hard alignment is used to achieve one-to-one matching between source and target vectors, then the alignment process is simple and deterministic, but alignment errors are introduced and magnified, reducing transformation quality
Solution Approach 1:
The patent changes the alignment parameter from binary (0 or 1) to continuous probability values between 0 and 1. This allows the system to express partial alignments and uncertainties, transforming the rigid one-to-one constraint into a flexible probabilistic relationship that better handles variations in speech features while maintaining computational tractability
Solution Approach 2:
The patent introduces dynamic alignment probabilities that can vary continuously rather than being fixed. This dynamic approach allows the alignment to adapt to different speech segments and speaker characteristics, enabling the system to handle temporal variations and pronunciation differences without forcing rigid one-to-one mappings
2Extent of automation
If dynamic time warping is used for automatic alignment, then alignment can be performed without manual intervention, but the process becomes computationally complex and time-consuming
Solution Approach 1:
The patent changes the alignment representation from discrete DTW paths to probabilistic distributions. This parameter transformation simplifies the computational structure by replacing complex path optimization with probability calculations, enabling automatic alignment while reducing computational burden through more efficient mathematical operations
3Reliability
If hard alignment is used to ensure clear vector pairing, then the alignment process is deterministic, but small alignment errors are magnified into larger errors, degrading voice conversion quality
Solution Approach 1:
The patent applies beforehand cushioning by using probabilistic alignment to anticipate and compensate for potential alignment errors. By representing alignment as probabilities rather than deterministic mappings, the system prepares for and buffers against the effects of minor misalignments, preventing them from being magnified during the voice conversion process
Solution Approach 2:
The patent changes the alignment parameter from deterministic to probabilistic, allowing the system to handle uncertainty inherent in speech processing. This parameter transformation enables the system to maintain reliability through clear pairing while preventing error magnification by acknowledging and averaging over multiple possible alignments rather than committing to single rigid pairs
Data Source
AI summary
Systems and methods are provided for performing soft alignment in Gaussian mixture model (GMM) based and other vector transformations. Soft alignment may assign alignment probabilities to source and target feature vector pairs. The vector pairs and associated probabilities may then be used calculate a conversion function, for example, by computing GMM training parameters from the joint vectors and alignment probabilities to create a voice conversion function for converting speech sounds from a source speaker to a target speaker.