Phonological Clustering via Deep Learning for TTS
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Solution Overview
Problem
Conventional methods for categorizing phonemes are suboptimal as they treat phonemes as implicit morphemes, leading to incorrect semantic mapping and are manual, making them infrequent and inefficient.
Innovation Solution
A method and system for phonological clustering that uses deep learning to create a non-semantic model allowing overlapping categorization of phonemes, automatically embedding phonemes into a radial set without manual intervention, enabling the generation of a repository of sounds for text-to-speech systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods treat phonemes as implicit morphemes for categorization, then semantic mapping is performed, but categorization accuracy deteriorates due to incorrect semantic mapping
Solution Approach 1:
The patent extracts phonemes from their traditional morphemic context and processes them independently through automatic clustering. By separating phoneme categorization from semantic morpheme treatment, the system eliminates incorrect semantic mapping while maintaining phoneme-specific acoustic characteristics, thereby improving categorization accuracy without losing phonological information
Solution Approach 2:
The patent replaces manual phoneme categorization with an automated deep learning-based clustering system. This substitution eliminates human error and inconsistency in semantic mapping while maintaining the ability to accurately categorize phonemes based on acoustic features, thus improving measurement precision without information loss
2Ease of manufacture
If manual phoneme categorization is used, then semantic mapping can be performed, but productivity deteriorates due to manual effort requirements
Solution Approach 1:
The patent implements self-service through automated phoneme clustering that performs categorization without human intervention. The deep learning model automatically processes phoneme sequences, identifies patterns, and generates categorizations independently, eliminating manual effort while maintaining process simplicity and significantly improving productivity
Solution Approach 2:
The patent substitutes manual categorization operations with an automated computational system based on deep learning. This replacement maintains the simplicity of the categorization process while dramatically increasing efficiency by processing large volumes of phoneme data rapidly without human intervention
3Productivity
If phoneme categorization is performed frequently and efficiently, then productivity improves, but computational resources and system complexity increase
Solution Approach 1:
The patent segments the phoneme processing task into discrete sequential steps: phoneme extraction from text, sequence formation, clustering execution, and result generation. This segmentation allows each component to be optimized independently and processed efficiently, improving overall productivity while managing system complexity through modular architecture
Solution Approach 2:
The patent performs preliminary action by pre-processing text into phoneme sequences before clustering. This preparation step organizes data in advance, enabling the clustering algorithm to operate more efficiently on structured input, thereby improving productivity without proportionally increasing system complexity
Data Source
AI summary
Methods and systems for phonological clustering are disclosed. A method includes: segmenting, by a computing device, a sentence into a plurality of tokens; determining, by the computing device, a plurality of phoneme variants corresponding to the plurality of tokens; clustering, by the computing device, the plurality of phoneme variants; creating, by the computing device, an initial vectorization of the plurality of phoneme variants based on the clustering; embedding, by the computing device, the initial vectorization of the plurality of phoneme variants into a deep learning model; and determining, by the computing device, a radial set of phoneme variants using the deep learning model.