Gaussian Mixture Model Distance Definition in Text-to-Speech
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Solution Overview
Problem
Current text-to-speech conversion technologies face challenges in accurately defining distances between samples and clusters due to data sparseness and dispersiveness, particularly with existing distance metrics like Euclid and Mahalanobis distances, which struggle to simulate complex distributions effectively.
Innovation Solution
The implementation of a Gaussian Mixture Model (GMM) for distance definition in text-to-speech conversion systems, which uses probability distribution to better handle data sparseness and dispersiveness by describing complex distributions with a cluster of Gaussian models, allowing for more accurate clustering and speech synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If Euclid distance or Mahalanobis distance is used for distance definition in TTS, then the calculation is simple, but the ability to simulate complex distributions is poor and data dispersiveness cannot be effectively handled
Solution Approach 1:
The patent transforms the distance definition from simple geometric metrics (Euclid/Mahalanobis) to a probability-based metric using GMM. By changing the fundamental parameter from distance to probability density, the system can effectively model complex distributions while maintaining computational feasibility through the GMM framework.
Solution Approach 2:
The patent employs a composite modeling approach by combining multiple Gaussian distributions into a GMM to represent complex data distributions. This composite structure allows the system to capture multimodal and non-Gaussian characteristics that single-distance metrics cannot handle, improving distribution simulation accuracy.
2Productivity
If traditional distance metrics are used, then the computational efficiency is high, but the handling of data sparseness and dispersiveness is inadequate
Solution Approach 1:
The patent replaces the mechanical/geometric distance calculation system with a probabilistic system based on GMM. This substitution allows the system to naturally handle data sparseness and dispersiveness by evaluating probability densities rather than relying on geometric proximity, thereby improving cluster evaluation reliability while maintaining computational efficiency through efficient GMM evaluation methods.
3Measurement precision
If GMM is used for distance definition, then the distribution simulation accuracy is improved, but the model complexity increases
Solution Approach 1:
The patent segments the complex distribution modeling task into multiple simpler Gaussian components. By dividing the overall distribution into several Gaussian clusters, each with simple parameters, the system achieves high simulation accuracy while keeping individual component complexity low. The GMM framework combines these segments to represent the overall complex distribution.
4Reliability
If GMM model is applied, then the naturalness of voice output is enhanced, but the training and computation requirements increase
Solution Approach 1:
The patent enables the GMM model to learn distribution characteristics directly from the training data through self-organization during the training process. The model automatically adapts to the data distribution patterns without requiring extensive manual feature engineering or large amounts of annotated data, thereby achieving high voice naturalness with moderate training data requirements.
Data Source
AI summary
A method for distance definition in a text-to-speech conversion system by applying Gaussian Mixture Model (GMM) to a distance definition. According to an embodiment, the text that is to be subjected to text-to-speech conversion is analyzed to obtain a text with descriptive prosody annotation; clustering is performed for samples in the obtained text; and a GMM model is generated for each cluster, to determine the distance between the sample and the corresponding GMM model.