Foreign Language Pronunciation Correction via Waveform Matching
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Solution Overview
Problem
Conventional foreign language learning machines struggle with accurate pronunciation correction due to limitations in evaluating segmental and non-segmental characteristics, inability to distinguish individual accents and stress, and lack of personalized learning, leading to ineffective pronunciation feedback and high costs.
Innovation Solution
A foreign language learning apparatus and method that uses a TTS engine to generate waveforms for sentence input, calculates matching percentages by comparing user voice with stored waveforms, and provides customized learning based on Ebbinghaus' theory for enhanced pronunciation correction and individualized learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional voice recognition programs use HMM and acoustic models to evaluate pronunciation, then pronunciation comparison can be performed, but measurement precision deteriorates because errors attributable to personal pronunciation propensities are disregarded and stress or isochronism cannot be evaluated
Solution Approach 1:
The patent segments the pronunciation evaluation into multiple independent components: segmental characteristics (phonemes), non-segmental characteristics (stress, isochronism, accent), and suprasegmental features. Each component is evaluated separately with appropriate weighting, allowing comprehensive and accurate pronunciation assessment without losing individual characteristic information.
Solution Approach 2:
The patent changes the evaluation parameters from simple phoneme matching to a multi-dimensional parameter system including segmental accuracy, stress position, isochronism ratio, accent characteristics, and pronunciation speed. This parameter expansion enables reliable evaluation of personal pronunciation propensities while maintaining measurement precision.
2Device complexity
If uniform evaluation criteria are applied to all learners, then evaluation process is simplified, but adaptability deteriorates because individual characteristics such as accent, stress, and pronunciation speed cannot be evaluated
Solution Approach 1:
The patent implements dynamic evaluation criteria that adapt to each learner's characteristics. The system automatically adjusts evaluation weights based on individual pronunciation patterns, allowing the same evaluation system to accommodate diverse learners without manual configuration while maintaining comprehensive assessment capability.
Solution Approach 2:
The patent incorporates feedback mechanisms that provide learners with detailed information about their pronunciation characteristics including accent patterns, stress placement, and timing issues. This feedback enables learners to understand their individual pronunciation profile and make targeted improvements.
3Measurement precision
If 1:1 teaching method with foreign lecturers is used, then pronunciation correction accuracy is improved, but cost increases and accessibility deteriorates due to scheduled education requirements
Solution Approach 1:
The patent enables learners to conduct self-evaluation and self-correction through the voice recognition system. The automated evaluation provides immediate feedback on pronunciation accuracy, allowing learners to independently identify and correct errors without requiring scheduled instructor availability, thus maintaining accuracy while improving accessibility.
Solution Approach 2:
The system provides immediate automated feedback on pronunciation accuracy, replacing the delayed feedback inherent in scheduled 1:1 teaching. This instantaneous feedback mechanism maintains correction effectiveness while eliminating time and cost constraints associated with human instructors.
4Measurement precision
If segmental characteristics are evaluated using phoneme data, then pronunciation comparison is enabled, but measurement precision deteriorates because non-segmental characteristics such as stress, isochronism, and sentence structure cannot be evaluated
Solution Approach 1:
The patent segments the pronunciation evaluation into multiple independent components: segmental characteristics (phonemes), non-segmental characteristics (stress, isochronism, accent), and suprasegmental features. Each component is evaluated separately with appropriate weighting, allowing comprehensive and accurate pronunciation assessment without losing individual characteristic information.
Data Source
AI summary
A foreign language learning apparatus and method are provided. The foreign language learning apparatus includes a sentence input unit receiving a first sentence from a user; a linked letter detection unit detecting at least one letter corresponding to at least one linking rule; a linked letter removal unit removing the letter and generating a second sentence by inserting a linking code; a partial waveform generation unit generating one or more partial waveforms using the Text To Speech (TTS) engine; an input waveform generation unit converting a voice corresponding to the first sentence into an input waveform; and a matching degree calculation unit calculating a matching degree and a partial matching degree. This foreign language learning apparatus enables a user to effectively learn pronunciation of a foreign language.