Keyword Phone Sequence Generation for Confusing Pronunciation Detection
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Solution Overview
Problem
Existing speech processing techniques struggle to accurately distinguish target speech from confusing pronunciations due to the uncertainty and variability of language, requiring large speech databases that are not sufficient for real-life scenarios and personalized user demands.
Innovation Solution
A data processing method that converts speech into keyword phone sequences, using a similar pronunciation sequence generator to identify similar phone sequences, thereby improving the ability to distinguish confusing pronunciations without relying on large-scale databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large speech database is constructed to improve discrimination of confusing pronunciations, then the model's ability to distinguish target speech is improved, but the storage space requirement and system complexity increase significantly
Solution Approach 1:
The patent creates artificial confusing pronunciation samples by copying and transforming the phoneme sequences of target speeches. Instead of storing large databases of real confusing pronunciations, the system generates synthetic copies by replacing phonemes with acoustically similar alternatives, achieving effective discrimination training with minimal storage requirements
Solution Approach 2:
The patent changes the phoneme composition parameter of speeches by systematically replacing phonemes with similar-sounding alternatives. This parameter transformation generates diverse confusing pronunciation variants from a single target speech, enabling the model to learn discrimination without requiring extensive storage of pre-collected confusing speech data
2Adaptability or versatility
If a large speech database is constructed to cover more real-life scenarios, then the coverage of confusing pronunciations is improved, but the system complexity and data processing burden increase
Solution Approach 1:
The patent implements a dynamic phoneme substitution mechanism that adapts to different target speeches. The system maintains a phoneme similarity dictionary and dynamically generates confusing pronunciation variants based on the specific phoneme composition of each target speech, enabling flexible adaptation to various real-life scenarios without requiring complex static databases
Solution Approach 2:
The patent segments speeches into phoneme units and processes each phoneme independently for substitution. This segmentation approach allows the system to generate diverse confusing pronunciation variants by combining different phoneme replacement options, achieving comprehensive coverage of real-life confusing scenarios through systematic decomposition and recombination rather than storing complete speech examples
3Measurement precision
If multiple speeches with highest similarity are labeled as confusing pronunciations and added to model training, then the model's discrimination ability is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary organization of phoneme similarity relationships by creating a phoneme similarity dictionary before actual model training. This pre-computation of phoneme substitution rules enables rapid generation of confusing pronunciation variants during training without requiring real-time similarity calculation, significantly reducing processing time while maintaining discrimination effectiveness
Data Source
AI summary
A data processing method, a device wake-up method, an electronic device, and a storage medium are provided. In the data processing method, speech to be processed is converted into a keyword phone sequence, and a similar pronunciation sequence generator acquires a similar phone sequence corresponding to the keyword phone sequence in a sequence generation manner, thereby acquiring a first data processed result corresponding to the speech to be processed. By replacing the search method of large-scale speech databases with this generation manner, effective coverage of possible real-life sounds can be achieved with a smaller model, thus improving the ability to distinguish confusing pronunciations. The above data processing method performed by the electronic device can be performed by an artificial intelligence (AI) model.